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Phoneme Classification in High-Dimensional Linear Feature Domains | 1312.6849 | TABLE III: Existing error rates obtained in other studies for a range of classification methods on the TIMIT core test set. Results in this paper are most comparable to the GMM baselines. | ['[BOLD] Method', '[BOLD] Error [%]'] | [['HMM (Minimum Classification Error)\xa0', '31.4'], ['GMM baseline\xa0', '26.3'], ['GMM baseline\xa0', '24.1'], ['GMM baseline\xa0', '23.4'], ['[BOLD] GMM ( [ITALIC] f-average + sector sum) PLP+Δ+ΔΔ', '[BOLD] 18.5'], ['SVM, 5th order polynomial kernel\xa0', '22.4'], ['Large Margin GMM (LMGMM)\xa0', '21.1'], ['Regulari... | We see that the best results for acoustic waveform classifiers are achieved around 9 frames, and around 11 frames for PLP without deltas. The PLP+Δ+ΔΔ features are less sensitive to the number of frames with little difference in error from 1 to 13 frames. We can now also assess quantitatively the performance benefit of... |
SenGen: Sentence Generating Neural Variational Topic Model | 1708.00308 | Table 1: Perplexity comparison of various models on two different datasets. All models are configured to use 25 topics. Lower is better. | ['Model', '20 Newsgroups', 'CNN/Daily Mail'] | [['LDA (Blei et\xa0al., 2003 )', '1247', '776'], ['NVDM (Miao et\xa0al., 2015 )', '757', '435'], ['NVLDA (Srivastava & Sutton, 2017 )', '1213', '592'], ['ProdLDA (Srivastava & Sutton, 2017 )', '1695', '735'], ['SenGen (Our Model)', '2354', '671']] | We compute perplexity of the test dataset using the trained SenGen model as follows: Perplexity=1NN∑d=1exp(−logP(wd|β)Nd) (9) where the log probability is computed using the lower-bound estimate in Eq. In the above equation, N is the number of test documents, and Nd is the number of words in document d. On 20 Newsgroup... |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations | 1905.02878 | Table 3: The influence of fine-tuning parser parameters in the SAWR system. | ['Parser', 'MT03', 'MT04', 'MT05', 'MT06', 'Average'] | [['no Tune', '[BOLD] 38.42', '[BOLD] 40.60', '[BOLD] 38.27', '[BOLD] 38.04', '[BOLD] 38.83'], ['Tune', '37.33', '39.45', '36.93', '37.03', '37.69']] | As an interesting attempt, we can simultaneously fine tune the parameters of both the parser and the Seq2Seq NMT model during training. We can see that fine-tuning decreases the average BLEU score by 38.83−37.69=1.14 significantly. This may be because that fine-tuning disorders the representation ability of the parser ... |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations | 1905.02878 | Table 4: Ensemble performances, where the Hybrid model denotes SAWR + Tree-RNN + Tree-Linearization. | ['System', 'MT03', 'MT04', 'MT05', 'MT06', 'Average/Δ'] | [['Baseline×3', '40.90', '43.25', '40.64', '40.16', '41.24'], ['[BOLD] SAWR×3', '41.94', '44.59', '41.91', '41.97', '42.60/+1.36'], ['Tree-RNN×3', '42.03', '44.15', '41.50', '41.41', '42.27/+1.03'], ['Tree-Linearization×3', '41.74', '44.23', '41.32', '41.44', '42.18/+0.94'], ['[BOLD] Hybrid', '[BOLD] 42.72', '[BOLD] 45... | First, we can see that ensemble is one effective technique to improve the translation performances. More importantly, the results show that the heterogeneous ensemble achieves averaged BLEU improvements by 43.10−41.24=1.86 points, better than the gains achieved by all three homo-approach ensembles, denoting that the th... |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations | 1905.02878 | Table 5: Final results based on the transformer. Only the SAWR results are significantly better (p<0.05). | ['System', 'MT03', 'MT04', 'MT05', 'MT06', 'Average/Δ'] | [['Transformer', '40.45', '42.76', '40.09', '39.67', '40.74'], ['[BOLD] SAWR', '[BOLD] 41.63', '[BOLD] 43.60', '[BOLD] 41.68', '[BOLD] 40.21', '[BOLD] 41.78/+1.04'], ['Tree-RNN', '41.24', '43.38', '41.04', '40.02', '41.42/+0.68'], ['Tree-Linearization', '41.12', '43.02', '41.04', '39.86', '41.26/+0.52']] | As shown, the transformer results are indeed much better than RNN-based baseline. The BLEU scores show an average increase of 40.74−37.09=3.65. In addition, we can see that syntax information can still give positive influences based on the transformer. The SAWR approach can also outperform the baseline system significa... |
Handling Syntactic Divergence in Low-resource Machine Translation | 1909.00040 | Table 3: BLEU of our approach (Reorder) with different amount of parallel sentences of ja-en and ug-en translation. Baselines are supervised learning (sup), supervised learning with back translation (back) and data augmentation with translated original English sentences (No-Reorder). | ['Model', '3k NMT', '3k SMT', '6k NMT', '6k SMT', '10k NMT', '10k SMT', '20k NMT', '20k SMT', '400k NMT', '400k SMT', 'ug NMT', 'ug SMT'] | [['sup', '2.17', '6.36', '7.86', '8.70', '11.67', '10.68', '15.98', '12.11', '26.56', '18.62', '0.58', '1.46'], ['back', '2.27', '8.46', '5.40', '10.61', '13.50', '12.05', '16.05', '13.68', '–', '–', '0.42', '1.37'], ['No-Reorder', '6.46', '3.08', '9.73', '5.24', '12.57', '6.72', '15.56', '8.96', '–', '–', '3.24', '1.6... | We present the full results in Tab. For SMT, reordering has much better performance than no-reorder, but still lags behind the supervised counterpart. |
Graph Neural News Recommendation with Long-term and Short-term Interest Modeling | 1910.14025 | Table 2: Comparison of Different Models | ['Model', 'Adressa-1week AUC(%)', 'Adressa-1week F1(%)', 'Adressa-10week AUC(%)', 'Adressa-10week F1(%)'] | [['DMF', '55.66', '56.46', '53.20', '54.15'], ['DeepWide', '68.25', '69.32', '73.28', '69.52'], ['DeepFM', '69.09', '61.48', '74.04', '65.82'], ['DKN', '75.57', '76.11', '74.32', '72.29'], ['DAN', '75.93', '74.01', '76.76', '71.65'], ['GNewsRec', '[BOLD] 81.16', '[BOLD] 82.85', '[BOLD] 78.62', '[BOLD] 81.01']] | We attribute the significant superiority of our model to its three advantages: (1) Our model constructs a heterogeneous user-news-topic graph and learns better user and news embeddings with high-order information encoded by GNN. (2) Our model considers not only the long-term user interest but also the short-term intere... |
Graph Neural News Recommendation with Long-term and Short-term Interest Modeling | 1910.14025 | Table 4: Impact of different GNN layers of GNewsRec. | ['Model', 'Adressa-1week AUC(%)', 'Adressa-1week F1(%)', 'Adressa-10week AUC(%)', 'Adressa-10week F1(%)'] | [['GNewsRec-1 layer', '75.24', '72.17', '76.17', '71.92'], ['GNewsRec-2 layers', '[BOLD] 81.16', '[BOLD] 82.85', '[BOLD] 78.62', '[BOLD] 81.01'], ['GNewsRec-3 layers', '78.94', '80.36', '77.92', '80.11']] | We vary the number of GNN layers from 1 to 3. This is because 1-layer GNN can’t capture the higher-order relationships between users and news. Nevertheless, 3-layer GNN may bring massive noise to the model. Thus, we choose 2-layer GNN in our model GNewsRec. |
How Document Pre-processing affects Keyphrase Extraction Performance | 1610.07809 | Table 2: Maximum recall and average number of keyphrase candidates for each model. | ['[BOLD] Model TF×IDF', '[BOLD] Lvl 1 80.2%', '[BOLD] Lvl 1 7\u2009837', '[BOLD] Lvl 2 78.2%', '[BOLD] Lvl 2 6\u2009958', '[BOLD] Lvl 3 67.8%', '[BOLD] Lvl 3 2\u2009270'] | [['Kea', '80.2%', '3\u2009026', '78.2%', '2\u2009502', '67.8%', '912'], ['TopicRank', '70.9%', '742', '69.2%', '627', '57.8%', '241'], ['KP-Miner', '64.0%', '724', '61.8%', '599', '48.7%', '212'], ['WINGNUS', '75.2%', '1\u2009355', '73.0%', '1\u2009007', '63.0%', '403']] | Each model uses a distinct keyphrase candidate selection method that provides a trade-off between the highest attainable recall and the size of set of candidates. Syntax-based selection heuristics, as used by TopicRank and WINGNUS, are better suited to prune candidates that are unlikely to be keyphrases. As for KP-mine... |
Meta Multi-Task Learning for Sequence Modeling | 1802.08969 | Table 5: Accuracy rates of our models on three tasks for sequence tagging.† means evaluated by F1 score(%), ‡ means evaluated by accuracy(%). ⧫ is the model implemented in [Huang, Xu, and Yu2015] . | ['[EMPTY]', 'CoNLL2000†', 'CoNLL2003†', 'WSJ‡'] | [['Single Task Model:', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['LSTM+CRF⧫', '93.67', '89.91', '97.25'], ['Meta-LSTM+CRF', '93.71', '[BOLD] 90.08', '[BOLD] 97.30'], ['collobert2011natural\xa0(collobert2011natural)', '94.32', '89.59', '97.29'], ['Multi-Task Model:', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['LSTM-SSP-MTL+CRF', '94.... | As shown, our proposed Meta-LSTM performs better than our competitor models whether it is single or multi-task learning. |
Meta Multi-Task Learning for Sequence Modeling | 1802.08969 | Table 3: Accuracies of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines. ∗is from [Liu, Qiu, and Huang2017] | ['[BOLD] Task', '[BOLD] Single Task LSTM', '[BOLD] Single Task HyperLSTM', '[BOLD] Single Task MetaLSTM', '[BOLD] Single Task Avg.', '[BOLD] Multiple Tasks ASP-MTL∗', '[BOLD] Multiple Tasks PSP-MTL', '[BOLD] Multiple Tasks SSP-MTL', '[BOLD] Multiple Tasks Meta-MTL(ours)', '[BOLD] Transfer Meta-MTL(ours)'] | [['Books', '79.5', '78.3', '83.0', '80.2', '87.0', '84.3', '85.3', '87.5', '86.3'], ['Electronics', '80.5', '80.7', '82.3', '81.2', '89.0', '85.7', '87.5', '89.5', '86.0'], ['DVD', '81.7', '80.3', '82.3', '81.4', '87.4', '83.0', '86.5', '88.0', '86.5'], ['Kitchen', '78.0', '80.0', '83.3', '80.4', '87.2', '84.5', '86.5'... | With the help of meta knowledge, we observe an average improvement of 3.1% over the average accuracy of single models, and even better than other competitor multi-task models. This observation indicates that we can save the meta knowledge into a meta network, which is quite useful for a new task. |
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation | 2004.03133 | Table 2: WEAT hypothesis test results for five popular gender-biased word categories. The best performing model is indicated as boldface. The second-best model is indicated as underline. The absolute value of the effect size denotes the degree of bias, and the p-value denotes the statistical significance of the results... | ['Embeddings', 'B1 : career vs family p-value', 'B1 : career vs family Effect size', 'B2 : maths vs arts p-value', 'B2 : maths vs arts Effect size', 'B3 : science vs arts p-value', 'B3 : science vs arts Effect size', 'B4 : intelligence vs appearance p-value', 'B4 : intelligence vs appearance Effect size', 'B5 : strengt... | [['GloVe', '0.000', '1.605', '0.276', '0.494', '0.014', '1.260', '0.009', '0.706', '0.067', '0.640'], ['Hard-GloVe', '0.100', '0.842', '0.090', '-1.043', '0.003', '-0.747', '[BOLD] 0.693', '[BOLD] -0.121', '0.255', '0.400'], ['GN-GloVe', '0.000', '1.635', '0.726', '-0.169', '0.081', '1.007', '0.037', '0.595', '0.083', ... | To quantify the degree of gender bias, we apply the Word Embedding Association Test (WEAT) Caliskan et al. WEAT measures the effect size and the hypothesis statistics based on the gender-definition words and the well-known gender-stereotypical words set, such as strength and weakness. While all baseline models record w... |
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation | 2004.03133 | Table 1: Percentage of predictions for each category on gender relational analogy task. We can expect a high percentage for Definition and low percentages for Stereotype and None for well-debiased word embeddings. † and ∗ denote the statistically significant differences comparing with Hard-GloVe and Glove, respectively... | ['Embeddings', 'Sembias Definition ↑', 'Sembias Stereotype ↓', 'Sembias None ↓', 'Sembias subset Definition ↑', 'Sembias subset Stereotype ↓', 'Sembias subset None ↓'] | [['GloVe', '80.22', '10.91', '8.86', '57.5', '20.0', '22.5'], ['Hard-Glove', '87.95∗', '8.41', '3.64∗', '50.0', '32.5', '17.5'], ['GN-GloVe', '97.73†∗', '1.36†∗', '0.91†∗', '75.0†', '15.0', '10.0'], ['ATT-GloVe', '80.22', '10.68', '9.09', '60.0', '17.5', '22.5'], ['CPT-GloVe', '73.63', '5.68', '20.68', '45.0', '12.5', ... | 4.3.1 Sembias Analogy Test We perform the gender relational analogy test with the Sembias dataset Zhao et al. ; Jurgens et al. The dataset contains 440 instances, and each instance consists of four pairs of words: 1) a gender-definition word pair (Definition), 2) a gender-stereotype word pair (Stereotype), and 3,4) two... |
Higher-order Coreference Resolution with Coarse-to-fine Inference | 1804.05392 | Table 1: Results on the test set on the English CoNLL-2012 shared task. The average F1 of MUC, B3, and CEAFϕ4is the main evaluation metric. We show only non-ensembled models for fair comparison. | ['[EMPTY]', 'MUC Prec.', 'MUC Rec.', 'MUC F1', 'B3 Prec.', 'B3 Rec.', 'B3 F1', 'CEAF [ITALIC] ϕ4 Prec.', 'CEAF [ITALIC] ϕ4 Rec.', 'CEAF [ITALIC] ϕ4 F1', 'Avg. F1'] | [['martschat:2015', '76.7', '68.1', '72.2', '66.1', '54.2', '59.6', '59.5', '52.3', '55.7', '62.5'], ['clark:2015', '76.1', '69.4', '72.6', '65.6', '56.0', '60.4', '59.4', '53.0', '56.0', '63.0'], ['wiseman:2015', '76.2', '69.3', '72.6', '66.2', '55.8', '60.5', '59.4', '54.9', '57.1', '63.4'], ['wiseman:2016', '77.5', ... | We include performance of systems proposed in the past 3 years for reference. The baseline relative to our contributions is the span-ranking model from \newcitee2e-coref augmented with both ELMo and hyperparameter tuning, which achieves 72.3 F1. Our full approach achieves 73.0 F1, setting a new state of the art for cor... |
Query-based Attention CNN for Text Similarity Map | 1709.05036 | Table 2: Experiment result | ['Models', 'dev set', 'test set'] | [['One stage QACNN', '66.8', '-'], ['QACNN(no attention)', '69.6', '-'], ['QACNN(only word-level attention)', '72.5', '-'], ['QACNN(only sentence-level attention)', '75.1', '-'], ['QACNN(single)', '77.6', '75.84'], ['[BOLD] QACNN(ensemble)', '[BOLD] 79.0', '[BOLD] 79.99']] | In this experiment,we focused on the difference between one-stage QACNN and two-stage QACNN. For one-stage QACNN, we didn’t split an entire passage into sentences. That is, the shape of passage-query similarity map PQ and passage-choice similarity map PC are 2D rather than 3D. We convolved them directly on word-level a... |
Look at the First Sentence:Position Bias in Question Answering | 2004.14602 | Table 3: Position bias in different positions. Each model is trained on a biased SQuAD dataset (SQuADktrain) and evaluated on SQuADdev. | ['[EMPTY]', '[BOLD] SQuADdev EM', '[BOLD] SQuADdev F1', '[BOLD] SQuADdev EM', '[BOLD] SQuADdev F1', '[BOLD] SQuADdev EM', '[BOLD] SQuADdev F1', '[BOLD] SQuADdev EM', '[BOLD] SQuADdev F1'] | [['[BOLD] SQuAD [ITALIC] ktrain', '[ITALIC] k=2', '[ITALIC] k=2', '[ITALIC] k=3', '[ITALIC] k=3', '[ITALIC] k=4', '[ITALIC] k=4', '[ITALIC] k=5,6,...', '[ITALIC] k=5,6,...'], ['[BOLD] SQuAD [ITALIC] ktrain', '(20,593 samples)', '(20,593 samples)', '(15,567 samples)', '(15,567 samples)', '(10,379 samples)', '(10,379 sam... | Due to the blurred sentence boundaries, position bias is less problematic when k is large. We observe a similar trend in BERT and XLNet while a huge performance drop is observed in BiDAF even with a large k. |
Look at the First Sentence:Position Bias in Question Answering | 2004.14602 | Table 1: Performance of QA models trained on the biased SQuAD dataset (SQuADk=1train), and tested on SQuADdev. Δ denotes the difference in F1 score with SQuADtrain. See Section 2.1 for more details. | ['Training Data', '[BOLD] BiDAF EM', '[BOLD] BiDAF F1', '[BOLD] BiDAF Δ', '[BOLD] BERT EM', '[BOLD] BERT F1', '[BOLD] BERT Δ', '[BOLD] XLNet EM', '[BOLD] XLNet F1', '[BOLD] XLNet Δ'] | [['[BOLD] SQuADtrain', '66.51', '76.46', '[EMPTY]', '79.54', '87.51', '[EMPTY]', '80.69', '89.24', '[EMPTY]'], ['[BOLD] SQuADtrain\xa0(Sampled)', '58.76', '70.52', '-5.94', '73.64', '84.99', '-2.52', '80.07', '88.32', '-0.92'], ['[BOLD] SQuAD [ITALIC] k=1train', '21.44', '27.92', '-48.54', '29.10', '35.24', '-52.27', '... | The performance of recurrent models (BiDAF) and self-attentive models (BERT, XLNet) drop significantly compared to models trained on SQuADtrain or SQuADtrain (Sampled). On Average, F1 scores has dropped by 48.26% in all three models which shows position bias of existing QA models. The relative position encodings in XLN... |
EliXa: A modular and flexible ABSA platform | 1702.01944 | Table 2: Results obtained on the slot2 evaluation on restaurant data. | ['[BOLD] System (type)', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1 score'] | [['Baseline', '55.42', '43.4', '48.68'], ['EliXa (u)', '68.93', '71.22', '[BOLD] 70.05'], ['NLANGP (u)', '70.53', '64.02', '67.12'], ['EliXa (c)', '67.23', '66.61', '66.91'], ['IHS-RD-Belarus (c)', '67.58', '59.23', '63.13']] | The implementation of the clustering features looks for the cluster class of the incoming token in one or more of the clustering lexicons induced following the three methods listed above. If found, then we add the class as a feature. The Brown clusters only apply to the token related features, which are duplicated. We ... |
Contextualization of Morphological Inflection | 1905.01420 | Table 2: Accuracy of the models for various prediction settings. tag refers to tag prediction accuracy, and form to form prediction accuracy. Our model is joint; gold denotes form prediction conditioned on gold target morphological tags; the other columns are baseline methods. | ['Language', 'tag joint', 'form gold', 'form joint', 'form direct', 'form SM', 'form CPH'] | [['Bulgarian', '81.55', '91.89', '78.81', '71.5', '77.10', '76.94'], ['English', '89.58', '95.57', '90.41', '86.75', '86.53', '86.71'], ['Basque', '66.63', '82.19', '61.05', '59.74', '61.20', '60.23'], ['Finnish', '65.99', '86.53', '59.34', '51.21', '56.61', '56.40'], ['Gaelic', '68.33', '84.50', '69.53', '64.51', '68.... | Below we highlight two main lessons from our error analysis that apply to a wider range of generation tasks, e.g., machine translation and dialog systems. author=kat,color=green!40,size=,fancyline, caption=,todo: author=kat,color=green!40,size=,fancyline,caption=,Tab2: ADD discussion: results on Italian and Hindi are i... |
User Evaluation of a Multi-dimensional Statistical Dialogue System | 1909.02965 | Table 3: Test results on simulated data (same error rates as in training): task success rate (SuccRate), average dialogue length (AvgLen), average reward (AvgRew). | ['[BOLD] system', '[BOLD] SuccRate', '[BOLD] AvgLen', '[BOLD] AvgRew'] | [['[ITALIC] one-dim', '97.8%', '14.69', '66.36'], ['[ITALIC] multi-dim', '97.6%', '15.68', '64.97'], ['[ITALIC] trans-fixed', '96.8%', '15.08', '65.23'], ['[ITALIC] trans-adapt', '97.4%', '16.41', '64.20']] | To get a better picture of what we might expect during the human evaluation, we first ran evaluations with simulated data. As we hypothesised, the scores are very similar, the one-dim system only slightly outperforming the multi-dimensional systems. |
Riposte! A Large Corpus of Counter-Arguments | 1910.03246 | Table 3: The average Jaccard’s similarity scores between CAs for a single argument for each fallacy type. | ['[BOLD] Criteria Score', '0.61', '0.17', '0.35', '0.36', '[BOLD] Total 0.24'] | [] | How similar are the CAs across annotators? One design decision when building Riposte! was that with more annotators , we could collect a wide variety of diverse CAs for a single-argument regardless of the fallacy type. We first calculate the similarity of the CAs across annotators for a single argument. We then calcula... |
Riposte! A Large Corpus of Counter-Arguments | 1910.03246 | Table 4: BLEU scores of our baselines using gold fallacy type for topic (T), premise (P), and claim (C). | ['[BOLD] Baseline', '[BOLD] T', '[BOLD] C', '[BOLD] P', '[BOLD] T+P+C', '[BOLD] T+C', '[BOLD] T+P', '[BOLD] P+C'] | [['[BOLD] SO', '3.98', '6.37', '15.59', '13.56', '10.69', '13.76', '18.16'], ['[BOLD] seq2seq-i', '12.28', '12.31', '5.96', '14.54', '12.63', '13.37', '16.57'], ['[BOLD] seq2seq-o', '1.31', '1.05', '1.49', '4.78', '1.60', '1.53', '5.53']] | Our SO results indicate that workers mainly used the premise and claim when creating CAs. We observe that seq2seq-o ’s performance is low, indicating a simple model is not sufficient when unknown topics are introduced. |
Video Question Answering via Attribute-Augmented Attention Network Learning | 1707.06355 | Table 2. Experimental results on both open-ended and multiple-choice video question answering tasks. | ['Method', 'Open-ended VQA task question type What', 'Open-ended VQA task question type Who', 'Open-ended VQA task question type Other', 'Open-ended VQA task question type Total accuracy', 'Multiple-choice VQA task question type What', 'Multiple-choice VQA task question type Who', 'Multiple-choice VQA task question typ... | [['VQA+', '0.2097', '0.2486', '0.7010', '0.386', '0.5998', '0.3071', '0.8144', '0.574'], ['SAN+', '0.168', '0.224', '0.722', '0.371', '0.582', '0.288', '0.804', '0.558'], ['r-ANL(− [ITALIC] a)', '0.164', '0.231', '0.784', '0.393', '0.550', '0.288', '0.825', '0.554'], ['r-ANL(1)', '0.179', '0.235', '0.701', '0.372', '0.... | The hyperparameters and parameters which achieve the best performance on the validation set are chosen to conduct the testing evaluation. We report the average value of all the methods on three evaluation criteria. |
Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media | 2006.05908 | Table 3: Evaluation results with different preprocessing techniques | ['[BOLD] Data set [BOLD] Method', 'MUNLIV [BOLD] Recall', 'MUNLIV [BOLD] Precision', 'MUNLIV [BOLD] F1', 'BrexitVote [BOLD] Recall', 'BrexitVote [BOLD] Precision', 'BrexitVote [BOLD] F1'] | [['all tokens', '0.826', '0.463', '0.594', '1.000', '0.800', '0.889'], ['without punctuation', '0.913', '0.457', '0.609', '1.000', '0.727', '0.842'], ['without punctuation and stop-words', '0.696', '0.552', '0.615', '1.000', '0.800', '0.889']] | Even though there is an improvement in the performance measures with preprocessing, these results show that we can obtain good measures without preprocessing also. This ability will be helpful in situations where we cannot integrate direct preprocessing mechanisms such as removing stop words in a less commonly used lan... |
NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media | 1703.03609 | TABLE V: Weights of all features (using unsupervised approach); features are ranked based on their overall average weights. | ['Dataset - Weights', 'DEV', 'NR', 'ETF', 'BST', 'RES', 'PP1', 'ACS', 'MCS'] | [['Main', '0.0029', '0.0550', '0.0484', '0.0445', '0.0379', '0.0329', '0.0321', '0.0314'], ['Review-based', '0.0626', '0.0510', '0.0477', '0.0376', '0.0355', '0.0346', '0.0349', '0.0340'], ['Item-based', '0.0638', '0.0510', '0.0501', '0.0395', '0.0388', '0.0383', '0.0374', '0.0366'], ['User-based', '0.0630', '0.0514', ... | Iv-C3 Unsupervised Method One of the achievement in this study is that even without using a train set, we can still find the best set of features which yield to the best performance. As it is explained in Sec. As shown in Fig. (p-value=0.0208) and for SPeaglePlus this value reach 0.90 (p=0.0021). As another example for... |
NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media | 1703.03609 | TABLE IV: Weights of all features (with 5% data as train set); features are ranked based on their overall average weights. | ['Dataset - Weights', 'DEV', 'NR', 'ETF', 'BST', 'RES', 'PP1', 'ACS', 'MCS'] | [['Main', '0.0029', '0.0032', '0.0015', '0.0029', '0.0010', '0.0011', '0.0003', '0.0002'], ['Review-based', '0.0023', '0.0017', '0.0017', '0.0015', '0.0010', '0.0009', '0.0004', '0.0003'], ['Item-based', '0.0010', '0.0012', '0.0009', '0.0009', '0.0010', '0.0010', '0.0004', '0.0003'], ['User-based', '0.0017', '0.0014', ... | Features Weights Importance: Combination of these features can be a good hint for obtaining better performance. The results of the Main dataset show all the four behavioral features are ranked as first features in the final overall weights. In addition, as shown in the Review-based as well as other two datasets, DEV is... |
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines | 1803.07427 | TABLE II: Accuracy reported for speaker-exclusive (Sp-Ex) and speaker-inclusive (Sp-In) split for Concatenation-Based Fusion. IEMOCAP: 10-fold speaker-exclusive average. MOUD: 5-fold speaker-exclusive average. MOSI: 5-fold speaker-exclusive average. Legend: A stands for Audio, V for Video, T for Text. | ['Modality Combination', 'IEMOCAP Sp-In', 'IEMOCAP Sp-Ex', 'MOUD Sp-In', 'MOUD Sp-Ex', 'MOSI Sp-In', 'MOSI Sp-Ex'] | [['A', '66.20', '51.52', '–', '53.70', '64.00', '57.14'], ['V', '60.30', '41.79', '–', '47.68', '62.11', '58.46'], ['T', '67.90', '65.13', '–', '48.40', '78.00', '75.16'], ['T + A', '78.20', '70.79', '–', '57.10', '76.60', '75.72'], ['T + V', '76.30', '68.55', '–', '49.22', '78.80', '75.06'], ['A + V', '73.90', '52.15'... | IEMOCAP: As this dataset contains 10 speakers, we performed a 10-fold speaker-exclusive test, where in each round exactly one of the speakers was included in the test set and missing from train set. The same SVM model was used as before and accuracy was used as performance metric. MOUD: This dataset contains videos of ... |
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines | 1803.07427 | TABLE I: Person-Independent Train/Test split details of each dataset (≈ 70/30 % split). Note: X→Y represents train: X and test: Y; Validation sets are extracted from the shuffled train sets using 80/20 % train/val ratio. | ['Dataset', 'Train [ITALIC] utterance', 'Train [ITALIC] video', 'Test [ITALIC] utterance', 'Test [ITALIC] video'] | [['IEMOCAP', '4290', '120', '1208', '31'], ['MOSI', '1447', '62', '752', '31'], ['MOUD', '322', '59', '115', '20'], ['MOSI → MOUD', '2199', '93', '437', '79']] | They collected 80 product review and recommendation videos from YouTube. Each video was segmented into its utterances (498 in total) and each of these was categorized by a sentiment label (positive, negative and neutral). On average, each video has 6 utterances and each utterance is 5 seconds long. In our experiment, w... |
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines | 1803.07427 | TABLE IV: Accuracy reported for speaker-exclusive classification. IEMOCAP: 10-fold speaker-exclusive average. MOUD: 5-fold speaker-exclusive average. MOSI: 5-fold speaker-exclusive average. Legend: A represents Audio, V represents Video, T represents Text. | ['Modality Combination', 'IEMOCAP SVM', 'IEMOCAP bc-LSTM', 'MOUD SVM', 'MOUD bc-LSTM', 'MOSI SVM', 'MOSI bc-LSTM'] | [['A', '52.9', '[BOLD] 57.1', '51.5', '[BOLD] 59.9', '58.5', '[BOLD] 60.3'], ['V', '47.0', '[BOLD] 53.2', '46.3', '[BOLD] 48.5', '53.1', '[BOLD] 55.8'], ['T', '65.5', '[BOLD] 73.6', '49.5', '[BOLD] 52.1', '75.5', '[BOLD] 78.1'], ['T + A', '70.1', '[BOLD] 75.4', '53.1', '[BOLD] 60.4', '75.8', '[BOLD] 80.2'], ['T + V', '... | We evaluated SVM and bc-LSTM fusion with MOSI, MOUD, and IEMOCAP dataset. So, it is very apparent that consideration of context in the classification process has substantially boosted the performance. |
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity | 1804.07835 | Table 1: Count of valid submissions to and the numbering of each SemEval STS task from 2012 to 2017. | ['[BOLD] Year', '[BOLD] Task no.', '[BOLD] Submissions'] | [['2012', '#6', '89'], ['2013', '#6', '90'], ['2015', '#2', '74'], ['2016', '#1', '124'], ['2017', '#1', '85']] | Semantic similarity, or relating short texts in a semantic space – be those phrases, sentences or short paragraphs – is a task that requires systems to determine the degree of equivalence between the underlying semantics of the two texts. Although relatively easy for humans, this task remains one of the most difficult ... |
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions | 1806.05258 | Table 1: Comparison between the number of self-reported diagnosed users per condition in the dataset of coppersmith2015adhd and ours (smhd). | ['[BOLD] Condition', 'Twitter, (Copper- smith et al, 2015)', 'Reddit, smhd (ours)'] | [['ADHD', '102', '10,098'], ['Anxiety', '216', '8,783'], ['Autism', '[EMPTY]', '2,911'], ['Bipolar', '188', '6,434'], ['Borderline', '101', '[EMPTY]'], ['Depression', '393', '14,139'], ['Eating', '238', '598'], ['OCD', '100', '2,336'], ['PTSD', '403', '2,894'], ['Schizophrenia', '172', '1,331'], ['Seasonal Affective', ... | Our work has the following significant distinctions compared to existing social media datasets related to mental health. This makes the Twitter language use rather different from real life discussions. Instead, we use data from Reddit, an interactive discussion-centric forum without any length constraints. Our dataset ... |
Message Passing Attention Networks for Document Understanding | 1908.06267 | Table 2: Classification accuracies. Best performance per column in bold, *best MPAD variant. OOM: >16GB RAM. | ['[BOLD] Model', 'Reut.', 'BBC', 'Pol.', 'Subj.', 'MPQA', 'IMDB', 'TREC', 'SST-1', 'SST-2', 'Yelp’13'] | [['doc2vec [le2014distributed]', '95.34', '98.64', '67.30', '88.27', '82.57', '[BOLD] 92.5', '70.80', '48.7', '87.8', '57.7'], ['CNN [kim2014convolutional]', '97.21', '98.37', '[BOLD] 81.5', '93.4', '89.5', '90.28', '93.6', '48.0', '87.2', '64.89'], ['DAN [iyyer2015deep]', '94.79', '94.30', '80.3', '92.44', '88.91', '8... | : Ablation results. The n in nMP refers to the number of message passing iterations. For the baselines, we provide the scores reported in the original papers. Furthermore, we have evaluated some of the baselines on the rest of our benchmark datasets, and we also report these scores. MPAD reaches best performance on 5 o... |
Message Passing Attention Networks for Document Understanding | 1908.06267 | Table 3: Ablation results. The n in nMP refers to the number of message passing iterations. *vanilla model (MPAD in Table 2). | ['[BOLD] MPAD variant', 'Reut.', 'Pol.', 'IMDB'] | [['MPAD 1MP', '96.57', '79.91', '90.57'], ['MPAD 2MP*', '97.07', '80.24', '[BOLD] 91.30'], ['MPAD 3MP', '97.07', '80.20', '91.24'], ['MPAD 4MP', '[BOLD] 97.48', '80.52', '[BOLD] 91.30'], ['MPAD 2MP undirected', '97.35', '80.05', '90.97'], ['MPAD 2MP no master node', '96.66', '79.15', '91.09'], ['MPAD 2MP no renormaliza... | To understand the impact of some hyperparameters on performance, we conducted additional experiments on the Reuters, Polarity, and IMDB datasets, with the non-hierarchical version of MPAD. Number of MP iterations. First, we varied the number of message passing iterations from 1 to 4. We attribute this to the fact that ... |
Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text | 2004.06384 | Table 3: The results of different models on the MSRA dataset. × indicates that the model uses the BERT. | ['Model', 'P', 'R', 'F'] | [['Chen et\xa0al. ( 2006 )', '91.22', '81.71', '86.20'], ['Dong et\xa0al. ( 2016 )', '91.28', '90.62', '90.95'], ['Zhang and Yang ( 2018 )', '93.57', '92.79', '93.18'], ['Zhu and Wang ( 2019 )', '93.53', '92.42', '92.97'], ['Ding et\xa0al. ( 2019 )', '94.60', '94.20', '94.40'], ['Zhao et\xa0al. ( 2019 )×', '95.46', '95... | Our model UIcwsNN specializes in learning word-level representation, but rarely considers other-levels characteristics, such as long-distance temporal semantics. Therefore, it only achieves competitive performance on the formal text. But our model UIcwsNN+BERT realizes new state-of-the-art performance. |
Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text | 2004.06384 | Table 2: The F values of existing models on the WeiboNER dataset. ∗ indicates that the model utilizes external lexicons. ∘ indicates that the model adopts joint learning. The previous models do not use the BERT, so we show the results of our model without BERT. | ['Models', 'NAM', 'NOM', 'Overall'] | [['Peng and Dredze ( 2015 )∘', '51.96', '61.05', '56.05'], ['Peng and Dredze ( 2016 )∘', '55.28', '62.97', '58.99'], ['He and Sun ( 2017a )', '50.60', '59.32', '54.82'], ['He and Sun ( 2017b )', '54.50', '62.17', '58.23'], ['Zhang and Yang ( 2018 )∗', '53.04', '62.25', '58.79'], ['Cao et\xa0al. ( 2018 )∘', '54.34', '57... | Our model UIcwsNN significantly outperforms other models and achieves new state-of-the-art performance. The overall score of our model is generally more than 2% higher than the scores of other models. Many methods use lexicon instead of the CWS to provide extractors with external word-level information, but how to choo... |
A Hierarchical Approach for Generating Descriptive Image Paragraphs | 1611.06607 | Table 2: Main results for generating paragraphs. Our Region-Hierarchical method is compared with six baseline models and human performance along six language metrics. | ['[EMPTY]', 'METEOR', 'CIDEr', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4'] | [['Sentence-Concat', '12.05', '6.82', '31.11', '15.10', '7.56', '3.98'], ['Template', '14.31', '12.15', '37.47', '21.02', '12.30', '7.38'], ['DenseCap-Concat', '12.66', '12.51', '33.18', '16.92', '8.54', '4.54'], ['Image-Flat ()', '12.82', '11.06', '34.04', '19.95', '12.20', '7.71'], ['Regions-Flat-Scratch', '13.54', '... | We present our main results at generating paragraphs in Tab. The Sentence-Concat method performs poorly, achieving the lowest scores across all metrics. Its lackluster performance provides further evidence of the stark differences between single-sentence captioning and paragraph generation. Surprisingly, the hard-coded... |
Unsupervised Text Style Transfer using Language Models as Discriminators | 1805.11749 | Table 1: Decipherment results measured in BLEU. Copy is directly measuring y against x. LM + adv denotes we use negative samples to train the language model.∗We run the code open-sourced by the authors to get the results. | ['Model', '20%', '40%', '60%', '80%', '100%'] | [['Copy', '64.3', '39.1', '14.4', '2.5', '0'], ['Shen et\xa0al. ( 2017 )∗', '86.6', '77.1', '70.1', '61.2', '[BOLD] 50.8'], ['Our results:', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['LM', '89.0', '[BOLD] 80.0', '[BOLD] 74.1', '62.9', '49.3'], ['LM + adv', '[BOLD] 89.1', '79.6', '71.8', '[BOLD] 63.8', '4... | We can see that using adversarial training sometimes improves the results. However, we found empirically that using negative samples makes the training very unstable and the model diverges easily. This is the main reason why we did not get consistently better results by incorporating adversarial training. |
Unsupervised Text Style Transfer using Language Models as Discriminators | 1805.11749 | Table 2: Results for sentiment modification. X=negative,Y=positive. PPLx denotes the perplexity of sentences transferred from positive sentences evaluated by a language model trained with negative sentences and vice versa. | ['Model', 'Accu', 'BLEU', 'PPL [BOLD] X', 'PPL [BOLD] Y'] | [['Shen et\xa0al. ( 2017 )', '79.5', '12.4', '50.4', '52.7'], ['Hu et\xa0al. ( 2017a )', '87.7', '[BOLD] 65.6', '115.6', '239.8'], ['Our results:', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['LM', '83.3', '38.6', '[BOLD] 30.3', '[BOLD] 42.1'], ['LM + Classifier', '[BOLD] 91.2', '57.8', '47.0', '60.9']] | Results: We report the results in Table. As a baseline, the original corpus has perplexity of 35.8 and 38.8 for the negative and positive sentences respectively. This demonstrates the effectiveness of using LM as the discriminator. has the highest accuracy and BLEU score among the three models while the perplexity is v... |
Unsupervised Text Style Transfer using Language Models as Discriminators | 1805.11749 | Table 3: Results for sentiment modification based on the 500 human annotated sentences as ground truth from (Li et al., 2018). | ['Model', 'ACCU', 'BLEU', 'PPL [BOLD] X', 'PPL [BOLD] Y'] | [['Shen et\xa0al. ( 2017 )', '76.2', '6.8', '49.4', '45.6'], ['Fu et\xa0al. ( 2017 ):', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['StyleEmbedding', '9.2', '16.65', '97.51', '142.6'], ['MultiDecoder', '50.9', '11.24', '111.1', '119.1'], ['Li et\xa0al. ( 2018 ):', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Delete... | Their method is feature based and consists of the following steps: (Delete) first, they use the statistics of word frequency to delete the attribute words such as “good, bad” from original sentences, (Retrieve) then they retrieve the most similar sentences from the other corpus based on nearest neighbor search, (Genera... |
Unsupervised Text Style Transfer using Language Models as Discriminators | 1805.11749 | Table 4: Related language translation results measured in BLEU. The results for sr vs bs in measured in BLEU1 while cn vs tw is measure in BLEU. | ['Model', 'sr–bs', 'bs–sr', 'cn–tw', 'tw–cn'] | [['Copy', '0', '0', '32.3', '32.3'], ['Shen et\xa0al. ( 2017 )', '29.1', '30.3', '60.1', '60.7'], ['Our results:', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['LM', '[BOLD] 31.0', '[BOLD] 31.7', '[BOLD] 81.6', '[BOLD] 85.5']] | Results: The results are shown in Table. For sr–bos and bos–sr, since the vocabulary of two languages does not overlap at all, it is a very challenging task. We report the BLEU1 metric since the BLEU4 is close to 0. The case for zh–tw and tw–zh is much easier. Simple copying already has a reasonable score of 32.3. |
Emotional Voice Conversion using multitask learning with Text-to-speech | 1911.06149 | Table 1: WER comparison | ['[EMPTY]', 'VC', 'VCTTS-V', 'VCTTS-T', 'TTS'] | [['WER', '54.54', '38.50', '31.98', '30.39']] | Word error rate (WER) was computed to measure how our proposed model improves the linguistic consistency of the converted speech. Practically, morphemes were used instead of words since morphemes are considered as recognition units of Korean speech [KwonP03, LeeC04, BangKK18]. Google Cloud Speech-to-Text API transcribe... |
Dual Co-Matching Network for Multi-choice Reading Comprehension | 1901.09381 | Table 2: Experiment results on RACE test set. † means it is statistically significant to the models ablating either the bidirectional matching or gated mechanism. ∗ indicates ensemble model. DMNbase uses BERTbase as encoder and DMNlarge uses BERTlarge as encoder. | ['[BOLD] Model', 'RACE-M', 'RACE-H', 'RACE'] | [['DFN Xu et\xa0al. ( 2017 )', '51.5', '45.7', '47.4'], ['HAF Zhu et\xa0al. ( 2018 )', '45.0', '46.4', '46.0'], ['MRU Tay et\xa0al. ( 2018 )', '57.7', '47.4', '50.4'], ['HCM Wang et\xa0al. ( 2018 )', '55.8', '48.2', '50.4'], ['MMN Tang et\xa0al. ( 2019 )', '61.1', '52.2', '54.7'], ['GPT Radford ( 2018 )', '62.9', '57.4... | Turkers is the performance of Amazon Turkers on a random subset of the RACE test set. Ceiling is the percentage of unambiguous questions in the test set. Comparison results show that our model is powerful and even the sing model outperforms all baselines and achieves new state-of-the-art accuracy. Our ensemble model fu... |
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search | 1805.08159 | Table 3: Main results on the TREC Microblog 2011–2014 datasets. Rows are numbered in the first column, where each represents a model or a contrastive condition. The last row shows the relative improvement against QL. The best numbers on each dataset are in bold. Superscripts and subscripts indicate the row indexes for ... | ['[BOLD] ID', '[BOLD] Model [BOLD] Metric', '[BOLD] 2011 [BOLD] MAP', '[BOLD] 2011 [BOLD] P30', '[BOLD] 2012 [BOLD] MAP', '[BOLD] 2012 [BOLD] P30', '[BOLD] 2013 [BOLD] MAP', '[BOLD] 2013 [BOLD] P30', '[BOLD] 2014 [BOLD] MAP', '[BOLD] 2014 [BOLD] P30'] | [['[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines', '[BOLD] Non-Neural Baselines'], ['1', '... | Rows are numbered in the first column, where each represents a model or a contrastive condition. We compare our model to three sets of baselines: non-neural, neural, and interpolation. Interpolation methods are denoted by a symbol “+” at the end of the original model name, such as DUET+. Superscripts and subscripts ind... |
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks | 1710.10393 | Table 4: Results of Label Embedding for the IWSLT2015 machine translation task. The evaluation metric is BLEU score (higher is better). | ['[BOLD] IWSLT2015', 'BLEU'] | [['Stanford NMT (Luong & Manning, 2015 )', '23.3'], ['NMT (greedy) (Luong et\xa0al., 2017 )', '25.5'], ['NMT (beam=10) (Luong et\xa0al., 2017 )', '26.1'], ['Seq2seq-Attention (beam=10)', '25.7'], ['[BOLD] Seq2seq-Attention-LabelEmb (beam=10)', '[BOLD] 26.8 (+1.1)']] | Then, we show experimental results on the IWSLT2015 machine translation task. We measure the quality of the translation by BLEU, following common practice. The proposed method achieves better BLEU score than the baseline, with an improvement of 1.1 points. To our knowledge, 26.8 is the highest BLEU achieved on the task... |
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks | 1710.10393 | Table 3: Results of Label Embedding for the LCSTS text summarization task (W: Word model; C: Character model). The evaluation metric is ROUGE score (higher is better). | ['[BOLD] LCSTS', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L'] | [['Seq2seq (W)\xa0(Hu et\xa0al., 2015 )', '17.7', '8.5', '15.8'], ['Seq2seq (C)\xa0(Hu et\xa0al., 2015 )', '21.5', '8.9', '18.6'], ['Seq2seq-Attention (W)\xa0(Hu et\xa0al., 2015 )', '26.8', '16.1', '24.1'], ['Seq2seq-Attention (C)\xa0(Hu et\xa0al., 2015 )', '29.9', '17.4', '27.2'], ['Seq2seq-Attention (C) (our impl... | First, we show experimental results on the LCSTS text summarization task. The performance is measured by ROUGE-1, ROUGE-2, and ROUGE-L. As we can see, the proposed method performs much better compared to the baselines, with ROUGE-1 score of 31.7, ROUGE-2 score of 19.1, and ROUGE-L score of 29.1, improving by 1.6, 1.2, ... |
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization | 1805.10985 | Table 4: Within-document test set results on ECB+. Note that Lemma is equivalent to Lemma-δ in the within-document setting. Cybulska and Vossen Cybulska and Vossen (2015) did not report the performance of their model in this setting. | ['[BOLD] Model', 'R', 'MUC P', 'F', 'R', 'B3 P', 'F', 'CM F', 'R', 'CE P', 'F', 'R', 'BLANC P', 'F', 'CoNLL F'] | [['[BOLD] Baselines', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Lemma- [ITALIC] δ', '41', '77', '53', '86', '97', '[BOLD] 92', '85', '92', '82', '87', '65', '86', '71', '77'], ['Unsupervised', '32', '36', ... | These results are obtained by cutting all links drawn across documents for the gold standard chains and the predicted chains. We observe that, across all models, scores on the mention- and entity-based measures are substantially higher than the link-based measures (e.g., MUC and BLANC). The usefulness of CORE+CCE+Lemma... |
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization | 1805.10985 | Table 2: Model comparison based on validation set B3 accuracy with optimized τ cluster-similarity threshold. For CORE+CCE+Lemma (indicated as CORE+CCE+L) we tuned to δ=0.89; for Lemma-δ we tuned to δ=0.67. | ['[BOLD] Model', '[ITALIC] λ1', '[ITALIC] λ2', 'B3', '[ITALIC] τ'] | [['[BOLD] Baselines', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Unsupervised', '-', '-', '0.590', '0.657'], ['Lemma', '-', '-', '0.597', '-'], ['Lemma- [ITALIC] δ', '-', '-', '0.612', '-'], ['[BOLD] Model Variants', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['CORE+CCE+L', '2.0', '0.0', '[BOLD] 0.678', '0.843'],... | Interestingly, we observe that CORE+CCE performs slightly better with λ2=0; i.e., without repulsive regularization. This suggests that enforcing representation similarity is more important than enforcing division, although we cannot conclusively state that repulsive regularization would not be useful for other tasks. N... |
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization | 1805.10985 | Table 3: Combined within- and cross-document test set results on ECB+. Measures CM and CE stand for mention-based CEAF and entity-based CEAF, respectively. | ['[BOLD] Model', 'R', 'MUC P', 'F', 'R', 'B3 P', 'F', 'CM F', 'R', 'CE P', 'F', 'R', 'BLANC P', 'F', 'CoNLL F'] | [['[BOLD] Baselines', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Lemma', '66', '58', '62', '66', '58', '62', '51', '87', '39', '54', '64', '61', '63', '61'], ['Lemma- [ITALIC] δ', '55', '68', '61', '61', '8... | and cross-document event coreference. Results for these models are obtained with the hyper-parameter settings that achieved optimal accuracy during validation-tuning. |
End-to-End Speech-Translation with Knowledge Distillation:FBK@IWSLT2020 | 2006.02965 | Table 2: Results on Librispeech with different K values, where K is the number of tokens considered for Word KD. | ['Top K', 'BLEU'] | [['4', '16.43'], ['8', '[BOLD] 16.50'], ['64', '16.37'], ['1024', '16.34']] | In this work, we follow Liu et al. , so the teacher model is our MT model and the student is the ST model. Compared to Liu et al. , we make the training more efficient by extracting only the top 8 tokens from the teacher distribution. In this way, we can precompute and store the MT output instead of computing it at eac... |
End-to-End Speech-Translation with Knowledge Distillation:FBK@IWSLT2020 | 2006.02965 | Table 1: Results on Librispeech with Word KD varying the number of layers. | ['2D Self-Attention', 'Encoder', 'Decoder', 'BLEU'] | [['2', '6', '6', '16.50'], ['0', '8', '6', '[BOLD] 16.90'], ['2', '9', '6', '17.08'], ['2', '9', '4', '17.06'], ['2', '12', '4', '[BOLD] 17.31']] | The ASR and ST models are a revisited version of the S-Transformer introduced by Di Gangi et al. Moreover, we noticed that adding more layers in the encoder improves the results, while removing few layers of the decoder does not harm performance. Hence, the models used in this work process the input with two 2D CNNs, w... |
End-to-End Speech-Translation with Knowledge Distillation:FBK@IWSLT2020 | 2006.02965 | Table 3: Case sensitive BLEU scores for our E2E ST models. Notes: Seq KD: Sequence KD; FT: finetuning on ground-truth datasets; TS: time stretch; Multi ENC: multi-domain model with sum of the language token to the encoder input; Multi DEC: multi-domain model with sum of the language token to the decoder input; DEC PT: ... | ['Model', 'MuST-C sentence', 'MuST-C VAD', 'IWSLT 2015'] | [['Seq KD+FT (w/o TS)', '25.80', '20.94', '17.18'], ['+ FT w/o KD', '27.55', '19.64', '16.93'], ['Multi ENC (w/o TS)', '25.79', '21.37', '19.07'], ['+ FT w/o KD', '27.24', '20.87', '19.08'], ['Multi ENC+DEC PT', '25.30', '20.80', '16.76'], ['+ FT w/o KD', '27.40', '21.90', '18.55'], ['Multi ENC+CTC', '[ITALIC] 27.06', ... | First, we compare the two training schemes examined. [Seq KD+FT] has the same performance as Multi-domain with language token summed to the input [Multi ENC] (or even slightly better) on the MuST-C test set, but it is significantly worse on the two test set segmented with VAD. This can be explained by the higher genera... |
Chinese Embedding via Stroke and Glyph Information:A Dual-channel View | 1906.04287 | Table 1: Performance on word similarity and word analogy task. The dimension of embeddings is set as 300. The evaluation metric is ρ for word similarity and accuracy percentage for word analogy. | ['Model', 'Word Similarity', 'Word Similarity', 'Word Analogy 3CosAdd', 'Word Analogy 3CosAdd', 'Word Analogy 3CosAdd', 'Word Analogy 3CosMul', 'Word Analogy 3CosMul', 'Word Analogy 3CosMul'] | [['Model', 'wordsim-240', 'wordsim-296', 'Capital', 'City', 'Family', 'Capital', 'City', 'Family'], ['Skipgram Mikolov et\xa0al. ( 2013 )', '0.5670', '0.6023', '[BOLD] 0.7592', '[BOLD] 0.8800', '0.3676', '[BOLD] 0.7637', '[BOLD] 0.8857', '0.3529'], ['CBOW Mikolov et\xa0al. ( 2013 )', '0.5248', '0.5736', '0.6499', '0.61... | We can observe that our DWE model achieves the best results both on dataset wordsim-240 and wordsim-296 in the similarity task as expected because of the particularity of Chinese morphology, but it only improves the accuracy for the family group in the analogy task. |
Simple, Fast Semantic Parsing with a Tensor Kernel | 1507.00639 | Table 1: Results on the WebQuestions dataset, together with results reported in the literature. | ['[EMPTY]', '[BOLD] Average F1 score'] | [['Sempre ', '35.7'], ['ParaSempre ', '39.9'], ['Facebook ', '41.8'], ['DeepQA ', '45.3'], ['Tensor kernel with unigrams', '40.1']] | Our system achieves an average F1 score of 40.1%, compared to ParaSempre’s 39.9%. Our system runs faster however, due to the simpler method of generating features. Evaluating using ParaSempre on the development set took 22h31m; using the tensor kernel took 14h44m on a comparable machine. |
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents | 1906.02128 | Table 1: Results in terms of precision, recall and F-score on 16 types of pronouns produced by the baseline systems and variants of our proposed NDPR model. For NRM∗ Zhang et al. (2016), we implement the proposed model as described in the paper. | ['Model', 'Chinese SMS P(%)', 'Chinese SMS R(%)', 'Chinese SMS F', 'TC of OntoNotes P(%)', 'TC of OntoNotes R(%)', 'TC of OntoNotes F', 'BaiduZhidao P(%)', 'BaiduZhidao R(%)', 'BaiduZhidao F'] | [['MEPR Yang et\xa0al. ( 2015 )', '37.27', '45.57', '38.76', '-', '-', '-', '-', '-', '-'], ['NRM∗ Zhang et\xa0al. ( 2016 )', '37.11', '44.07', '39.03', '23.12', '26.09', '22.80', '26.87', '49.44', '34.54'], ['BiGRU', '40.18', '45.32', '42.67', '25.64', '36.82', '30.93', '29.35', '42.38', '35.83'], ['NDPR-rand', '46.47... | We can see that our proposed model and its variants outperform the baseline methods on all these datasets by different margins. Our best model, NDPR, outperforms MEPR by 7.63% in terms of F-score on the Chinese SMS dataset, and outperforms NRM by 16.97% and 8.40% on the OntoNotes and BaiduZhidao datasets respectively. ... |
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents | 1906.02128 | Table 2: F-scores of our proposed model NDPR and its two variants (NDPR-S, NDPR-W) for concrete and abstract pronouns on the Chinese SMS test set. | ['Tag', 'NDPR-S', 'NDPR-W', 'NDPR'] | [['他们(masculine they)', '17.05', '23.28', '[BOLD] 24.44'], ['她(she)', '32.35', '33.72', '[BOLD] 35.14'], ['previous utterance', '84.90', '86.08', '[BOLD] 87.55'], ['他(he)', '29.05', '31.20', '[BOLD] 34.92'], ['它(it)', '25.00', '26.67', '[BOLD] 26.95'], ['她们(feminine they)', '0', '0', '[BOLD] 40.00'], ['我(I)', '50.66', ... | In this section, we dive a bit deeper and look at the impact of the attention mechanism on concrete and abstract pronouns, respectively. The best results among these three variants are in boldface, and the better results between NDPR-S and NDPR-W are underlined. |
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs | 1712.06204 | TABLE I: Area-Under-Curve (AUC) of precision-recall curves on VIRAT dataset with human annotated bounding boxes for Bag-of-Words approach (BoW [6]), Manually Specified Graph Matching (MSGM [5]), and our proposed approach. | ['Query', 'BoW ', 'MSGM ', 'Proposed'] | [['Person dismount', '15.33', '78.26', '[BOLD] 83.93'], ['Person mount', '21.37', '70.61', '[BOLD] 83.94'], ['Object deposit', '26.39', '71.34', '[BOLD] 85.69'], ['Object take-out', '8.00', '72.70', '[BOLD] 80.07'], ['2 person deposit', '14.43', '65.09', '[BOLD] 74.16'], ['2 person take-out', '19.31', '80.00', '[BOLD] ... | On human annotated data, where we assume no uncertainty at the object level, we can see that both MSGM and the proposed method significantly outperform BoW. The queries all include some level of structural constraints between objects, for example, there is an underlying distance constraint for the people, car and objec... |
Sub-band Knowledge Distillation Framework for Speech Enhancement | 2005.14435 | Table 3: Demonstrates the effectiveness of the sub-band knowledge distillation framework. C represents the number of memory cells per layer in LSTM. #Params represents the number of parameters of the model. | ['Model', 'C', '#Params (M)', 'PESQ', 'STOI (%)'] | [['Noisy', '-', '-', '1.971', '92.106'], ['F', '256', '2.52', '2.420', '93.412'], ['S1', '256', '2.21', '2.404', '93.133'], ['S2', '256', '2.21', '[BOLD] 2.471', '[BOLD] 93.751'], ['F', '512', '9.23', '2.511', '93.817'], ['S1', '512', '8.61', '2.497', '93.540'], ['S2', '512', '8.61', '[BOLD] 2.563', '[BOLD] 94.129']] | Since the input of the F model is the full-band features, the number of parameters of the F model is higher than that of the S1 model and S2 model. Whether the number of memory cells is 256 or 512, we can find some similar conclusions. (1) The performance of the S1 model is slightly worse than the F model. This may be ... |
Sub-band Knowledge Distillation Framework for Speech Enhancement | 2005.14435 | Table 2: The mean square error (MSE, ℓ2 loss) of the teacher models and the student models (without the guidance of the teacher models) with the different number of memory cells per layer in LSTM for each sub-band on the test set. C is the number of memory cells per layer in LSTM. | ['C', 'Student Model 0-40', 'Student Model 40-80 (10−4)', 'Student Model 80-120 (10−4)', 'Student Model 120-160 (10−4)', 'Teacher Model 0-40', 'Teacher Model 40-80 (10−4)', 'Teacher Model 80-120 (10−4)', 'Teacher Model 120-160 (10−4)'] | [['256', '0.036', '10.027', '3.647', '2.801', '0.031', '9.230', '3.578', '2.204'], ['512', '0.026', '9.701', '3.594', '2.508', '0.019', '9.038', '3.494', '1.792'], ['1024', '0.025', '9.162', '3.295', '2.095', '[BOLD] 0.013', '[BOLD] 8.716', '[BOLD] 3.020', '[BOLD] 1.634']] | C is the number of memory cells. The left half of the table is the result area of the student models, and the right half is the result area of the teacher models. For example, in the result area of the student models, the value in the first column (0 to 40) of the first row (C is 256) shows the result of the student mo... |
A Generative Model for Punctuation in Dependency Trees | 1906.11298 | Table 3: Results of the conditional perplexity experiment (Section 4), reported as perplexity per punctuation slot, where an unpunctuated sentence of n words has n+1 slots. Column “Attn.” is the BiGRU tagger with attention, and “CRF” stands for the BiLSTM-CRF tagger. “Attach” is the ablated version of our model where s... | ['[EMPTY]', 'Attn.', 'CRF', 'Attach', '+NC', 'Dir'] | [['Arabic', '1.4676', '1.3016', '1.2230', '[BOLD] 1.1526', 'L'], ['Chinese', '1.6850', '1.4436', '1.1921', '[BOLD] 1.1464', 'L'], ['English', '1.5737', '[BOLD] 1.5247', '1.5636', '[BOLD] 1.4276', 'R'], ['Hindi', '1.1201', '1.1032', '1.0630', '[BOLD] 1.0598', 'L'], ['Spanish', '1.4397', '[BOLD] 1.3198', '[BOLD] 1.2364',... | Also, in 4 of 5 languages, allowing a trained NoisyChannel (rather than the identity map) significantly improves the perplexity. |
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings | 1603.07185 | Table 3: Results from a CI query to find papers with related titles using proximityAvg(), proximityMax(), and proximityTop2Avg() | ['[BOLD] Query Results using ProximityAvg()', 'Cos. Dist.'] | [['Istvan_Cseri,Indexing XML Data Stored in a Relational Database.,VLDB_2004', '0.4048'], ['Rajeev_Rastogi,DataBlitz A High Performance Main-Memory Storage Manager.,VLDB_1998,', '0.3581'], ['Patricia_G._Selinger,Information Integration and XML in IBM’s DB2.,VLDB_2002', '0.3403'], ['Shinichi_Morishita,Relational-style X... | In this query, the title of the paper with number 471 is “Native Xquery processing in oracle XMLDB”. In this query, vectors for different tokens in a title are compared in three ways to identify similar titles: proximityAvg() selects the title whose average vectors are close, whereas proximityMax() uses maximum closene... |
Bidirectional Recurrent Models for Offensive Tweet Classification | 1903.08808 | Table 1: Holdout-validation macro F1 scores and accuracy of all models for the three different tasks. | ['[EMPTY]', '[BOLD] Task A [BOLD] F1', '[BOLD] Task A [BOLD] Acc', '[BOLD] Task B [BOLD] F1', '[BOLD] Task B [BOLD] Acc', '[BOLD] Task C [BOLD] F1', '[BOLD] Task C [BOLD] Acc'] | [['[BOLD] biLSTM', '0.7382', '0.7801', '0.6115', '0.5753', '0.5001', '0.6966'], ['[BOLD] CNN-biLSTM', '0.6346', '0.6473', '0.5521', '0.5068', '0.4322', '0.6142'], ['[BOLD] biLSTM-CNN', '0.7170', '0.7562', '0.5496', '0.4903', '0.4738', '0.6863'], ['[BOLD] biGRU ⊕ biLSTM', '0.7285', '0.7544', '0.5963', '0.5722', '0.5052'... | For most tasks, the most simple biLSTM model outperformed the other architectures, with the more complex biGRU⊕biLSTM closely following. We learned that, at least for this task and data set, more complex models did not necessarily result in better performance. The biLSTM model’s macro F1 scores on the OffensEval privat... |
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation | 1905.12926 | Table 4: Results for multi-aspect attribute transfer. The kappa coefficient of the three workers is 0.67 ∈ (0.61, 0.80), which means that the consistency is substantial. | ['Aspects', 'Acc', 'Att', 'Con', 'Gra'] | [['Appearance', '90.2%', '3.2', '3.5', '3.8'], ['Aroma', '89.3%', '3.4', '3.9', '3.7'], ['Palate', '91.2%', '3.1', '3.8', '3.7'], ['Taste', '88.2%', '3.4', '3.7', '3.6'], ['Overall', '87.3%', '3.6', '4.0', '3.8']] | We see that the achieved sentiment accuracy is high, which means that our model can perform sentiment transfer over multiple aspects at the same time. Considering the results of human evaluation, our model has good fluency and preservation of content when performing sentiment transferring over multiple aspects. To the ... |
GPT-too: A language-model-first approach for AMR-to-text generation | 2005.09123 | Table 1: Results on the LDC2017T10 development set using GPT-2 S(mall) and M(edium) with Rec(onstruction) loss (see §2) for different AMR representations (see §4). | ['Model', 'Input', 'BLEU', 'chrF++'] | [['GPT-2S\xa0Rec.', 'Only nodes AMR', '9.45', '41.59'], ['GPT-2S\xa0Rec.', 'Lin. AMR w/o edges.', '11.35', '43.25'], ['GPT-2S\xa0Rec.', 'Lin. AMR w/edges.', '20.14', '53.12'], ['GPT-2S\xa0Rec.', 'Penman AMR', '22.37', '53.92'], ['GPT-2M\xa0Rec.', 'Lin. AMR w/edges.', '22.86', '55.04'], ['GPT-2M\xa0Rec.', 'Penman AMR', ... | Edge information, indicating relations between concepts, seems also to play a fundamental role since its absence strongly decreases performance in both DFS and PENMAN representations. Penman notation was chosen for the rest of the experiments. |
GPT-too: A language-model-first approach for AMR-to-text generation | 2005.09123 | Table 2: Results on the LDC2017T10 development set. Rec(onstruction) uses the AMR reconstruction term (see §2) whereas Conditional does not. | ['Approach', 'Decoding', 'BLEU', 'chrF++'] | [['GPT-2M\xa0Conditional', 'Greedy', '25.73', '57.2'], ['GPT-2M\xa0Rec.', 'Greedy', '30.41', '61.36'], ['GPT-2M\xa0Rec.', 'BEAM', '31.8', '62.56'], ['GPT-2M\xa0Rec.', 'BEAM 10', '[BOLD] 32.32', '62.79'], ['GPT-2M\xa0Rec.', 'Sampling', '28.75', '61.19']] | The model trained using this additional term achieves 30.41 BLEU and 61.36 chrF++, as opposed to 25.73 BLEU and 57.2 chrF++ without the term. We therefore use a reconstruction term training in the rest of the experiments. With beam size 10, we obtain 32.32 BLEU and 62.79 chrF++. With nucleus sampling at a cumulative pr... |
On the Variance of the Adaptive Learning Rate and Beyond | 1908.03265 | Table 2: Performance on CIFAR10 (lr = 0.1). | ['1-4 steps', '5-8 steps', '8+ steps', 'test acc', 'train loss', 'train error'] | [['RAdam', 'RAdam', 'RAdam', '91.08', '0.021', '0.74'], ['Adam (w. divergent var.)', 'RAdam', 'RAdam', '89.98', '0.060', '2.12'], ['SGD', 'Adam (w. convergent var.)', 'RAdam', '90.29', '0.038', '1.23']] | As a byproduct determined by math derivations, we degenerated RAdam to SGD with momentum in the first several updates. Intuitively, updates with divergent adaptive learning rate variance could be more damaging than the ones with converged variance, as divergent variance implies more instability. As a case study, we per... |
On the Variance of the Adaptive Learning Rate and Beyond | 1908.03265 | Table 1: BLEU score on Neural Machine Translation. | ['Method', 'IWSLT’14 DE-EN', 'IWSLT’14 EN-DE', 'WMT’16 EN-DE'] | [['Adam with warmup', '34.66±0.014', '28.56±0.067', '27.03'], ['RAdam', '34.76±0.003', '28.48±0.054', '27.27']] | With a consistent adaptive learning rate variance, our proposed method achieves similar performance to that of previous state-of-the-art warmup heuristics. It verifies our intuition that the problematic updates of Adam are indeed caused by the undesirably large variance in the early stage. |
Hero: Hierarchical Encoder for Video+Language Omni-representation Pre-training | 2005.00200 | Table 2: Comparison between a flat BERT-like encoder (F-Trm), a Hierarchical Transfomrer (H-Trm) baseline and Hero using TVR and TVQA validation set as benchmarks. Results in the last two rows are obtained from pre-training the models with MLM + MNCE + FOM + VSM on TV Dataset. For simplicity, we report only video momen... | ['Pre-training', 'Model', 'R@1', 'TVR R@10', 'R@100', 'TVQA Acc.'] | [['No', 'F-Trm', '1.99', '7.76', '13.26', '31.80'], ['No', 'H-Trm', '2.97', '10.65', '18.68', '70.09'], ['No', 'Hero', '2.98', '10.65', '18.25', '70.65'], ['Yes', 'H-Trm', '3.12', '11.08', '18.42', '70.03'], ['Yes', 'Hero', '[BOLD] 4.44', '[BOLD] 14.69', '[BOLD] 22.82', '[BOLD] 72.75']] | (i) When no pre-training is applied, F-Trm is much worse than Hero on both tasks. H-Trm achieves comparable results to Hero on TVR, but worse on TVQA. Unlike F-Trm, H-Trm and Hero explicitly utilize the inherent temporal alignment between two modalities of videos, which is uniquely important for video+language tasks. (... |
Hero: Hierarchical Encoder for Video+Language Omni-representation Pre-training | 2005.00200 | Table 1: Evaluation on pre-training tasks and datasets using TVR, TVQA, Howto100M-R and Howto100M-QA validation set as benchmarks. Dark and light grey colors highlight the top and second best results across all the tasks trained with TV Dataset. The best results are in bold. For simplicity, we only report video moment ... | ['Pre-training Data', '[EMPTY]', 'Pre-training Tasks', 'TVR R@1', 'TVR R@10', 'TVR R@100', 'TVQA Acc.', 'Howto100M-R R@1', 'Howto100M-R R@10', 'Howto100M-R R@100', 'Howto100M-QA Acc.'] | [['TV', '1', 'MLM', '2.92', '10.66', '17.52', '71.25', '2.06', '9.08', '14.45', '76.42'], ['TV', '2', 'MLM + MNCE', '3.13', '10.92', '17.52', '71.99', '2.15', '9.27', '14.98', '76.95'], ['TV', '3', 'MLM + MNCE + FOM', '3.09', '10.27', '17.43', '72.54', '2.36', '9.85', '15.97', '77.12'], ['TV', '4', 'MLM + MNCE + FOM + ... | To evaluate the effectiveness of each pre-training task, we conduct ablation experiments through pre-training on TV dataset only. When MLM, MNCE and FOM are jointly trained (L3), there is a large performance gain in accuracy on TVQA and significant improvement on the two Howto100M downstream tasks. Comparable results a... |
Hero: Hierarchical Encoder for Video+Language Omni-representation Pre-training | 2005.00200 | Table 3: Results on four downstream tasks: TVR, Howto100M-R, TVQA and Howto100M-QA, compared with task-specific state-of-the-art method: XML for TVR and STAGE for TVQA. Only video moment retrieval results are reported for TVR and Howto100M-R. | ['Method', 'TVR R@1', 'TVR R@10', 'TVR R@100', 'Howto100M-R R@1', 'Howto100M-R R@10', 'Howto100M-R R@100', 'TVQA Acc.', 'Howto100M-QA Acc.'] | [['XML\xa0(Lei et al., 2020 )', '2.70', '8.93', '15.34', '2.06', '8.96', '13.27', '-', '-'], ['STAGE\xa0(Lei et al., 2019 )', '-', '-', '-', '-', '-', '-', '70.50', '-'], ['Hero w/o pre-training', '2.98', '10.65', '18.42', '2.17', '9.38', '15.65', '70.65', '76.89'], ['Hero w/ pre-training', '[BOLD] 4.34', '[BOLD] 13.... | First, we compare with XML Results show that our model consistently outperforms XML on both TVR and Howto100M-R, with or without pre-training. |
Differentially Private Distributed Learning for Language Modeling Tasks | 1712.07473 | Table 7: Results of the Lilliefors test | ['Experiment', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10'] | [['ˆ [ITALIC] α', '15.8', '20.9', '15.1', '16.6', '16.5', '17.6', '14.9', '19.2', '15.6', '15.2'], ['ˆ [ITALIC] C', '3.25', '5.64', '2.02', '2.48', '2.70', '4.19', '1.47', '3.31', '1.65', '1.83'], ['KS statistic', '0.49', '0.91', '0.48', '0.62', '0.83', '0.59', '[BOLD] 1.39', '0.41', '0.93', '0.51'], ['Experiment', '11... | The critical value for the Lilliefors test at 5% significance level is 1.08. In 19 cases out of 20 the Lilliefors test fails to reject the null hypothesis. Exact values of KS statistics and Hill ’s It converges to the distribution with smaller critical values at the same significance levels because we overfit on the sa... |
Differentially Private Distributed Learning for Language Modeling Tasks | 1712.07473 | Table 1: Random rehearsal vs learning without forgetting. For LwF mode λ is a coefficient of the ground truth probability distribution in the loss function (1)-(2). For random rehearsal mode λ is a portion of user training data in on-device training. | ['Method', 'Standard English dataset (Wikipedia) PPL', 'Standard English dataset (Wikipedia) KSS, %', 'User dataset (Twitter) PPL', 'User dataset (Twitter) KSS, %', 'Av. PPL'] | [['Initial server model', '100.1', '67.9', '336.0', '49.7', '192.6'], ['Random rehearsal, [ITALIC] λ=1/4', '121.3', '66.3', '127.9', '56.9', '124.8'], ['Random rehearsal, [ITALIC] λ=1/2', '131.1', '65.9', '109.7', '58.3', '[BOLD] 119.1'], ['Random rehearsal, [ITALIC] λ=3/4', '149.0', '64.8', '99.7', '59.0', '119.9'], ... | We see that the performance gap between the standard English and the user test sets can be considerably reduced at the cost of performance degradation on the first dataset. The best average perplexity is reached with the random rehearsal method and λ=0.5. We believe that the reason of the comparably inferior performanc... |
Differentially Private Distributed Learning for Language Modeling Tasks | 1712.07473 | Table 2: Averaging vs transfer learning for server-side model update. | ['Method', 'Standard English dataset (Wikipedia) PPL', 'Standard English dataset (Wikipedia) KSS, %', 'User dataset (Twitter) PPL', 'User dataset (Twitter) KSS, %', 'Av. PPL'] | [['Initial server model', '100.1', '67.9', '336.0', '49.7', '192.6'], ['TL on generated data (1-cycle)', '109.2', '67.2', '259.7', '50.8', '174.4'], ['TL on generated data (5-cycles)', '112.3', '67.0', '246.0', '51.2', '171.6'], ['TL on real data', '108.7', '67.2', '261.2', '50.7', '174.6'], ['Model averaging (1 round)... | We saw no significant differences between transfer learning on real and generated data. The difference between transfer learning and averaging is more sound but still not large. At the same time model averaging is much more computationally efficient, as long as transfer learning requires calculation of labels from each... |
Multi-Task Learning for Sequence Tagging: An Empirical Study | 1808.04151 | Table 6: F1 scores for Multi-Dec. We compare All with All-but-one settings (All - ⟨task⟩). We test on each task in the columns. Beneficial settings are in green. Harmful setting are in red. | ['[EMPTY]', 'upos', 'xpos', 'chunk', 'ner', 'mwe', 'sem', 'semtr', 'supsense', 'com', 'frame', 'hyp', '#↑', '#↓'] | [['All', '95.04', '94.31', '93.44', '86.38', '61.43', '71.53', '74.26', '68.1', '74.54', '59.71', '51.41', '[EMPTY]', '[EMPTY]'], ['All - upos', '[EMPTY]', '94.03', '93.59', '86.03', '61.28', '70.87', '73.54', '68.27', '74.42', '58.47', '51.13', '0', '0'], ['All - xpos', '94.57 ↓', '[EMPTY]', '93.57', '86.04', '61.91',... | How much does one particular task contribute to the performance of All MTL? To investigate this, we remove one task at a time and train the rest jointly. We find that upos, sem and semtr are in general sensitive to a task being removed from All MTL. Moreover, at least one task significantly contributes to the success o... |
Neural Machine Translation Decoding with Terminology Constraints | 1805.03750 | Table 3: Bleu scores and speed ratios relative to unconstrained Lnmt for production system with up to c constraints per sentence (newstest2017). A: secondary attention, B, C: allow 1 or 2 extra tokens, respectively (Section 2.3). Dict (v2∗) refers to decoding with attentions but without A, B or C. | ['[ITALIC] eng-ger-wmt17', 'Bleu [BOLD] /speed ratio [ITALIC] c=2', 'Bleu [BOLD] /speed ratio [ITALIC] c=2', 'Bleu [BOLD] /speed ratio [ITALIC] c=3', 'Bleu [BOLD] /speed ratio [ITALIC] c=3', 'Bleu [BOLD] /speed ratio [ITALIC] c=4', 'Bleu [BOLD] /speed ratio [ITALIC] c=4'] | [['Lnmt', '26.7', '1.00', '26.7', '1.00', '26.7', '1.00'], ['+ dict (v1)', '28.2', '0.20', '28.4', '0.14', '28.5', '0.11'], ['+ dict (v2∗)', '27.8', '0.69', '28.0', '0.66', '28.1', '0.59'], ['+ A', '28.0', '0.65', '28.2', '0.61', '28.2', '0.54'], ['+ B', '28.4', '0.27', '28.6', '0.24', '28.7', '0.21'], ['+ C', '28.5', ... | Tab. Rows two and three confirm that the reduced computational complexity of our approach yields faster decoding speeds than the approach of Anderson et al. while incurring a small decrease in Bleu. Moreover, it compares favourably for larger numbers of constraints per sentence: v2 * is 3.5x faster than v1 for c=2 and ... |
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension | 1706.04815 | Table 6: The performance on MS-MARCO development set of end-to-end methods. | ['[BOLD] Method', '[BOLD] ROUGE-L'] | [['S2S (Question)', '8.9'], ['S2S (Question + All Passages)', '28.75'], ['S2S (Question + Selected Passage)', '37.70'], ['Matching + S2S', '6.28']] | The authors of MS-MACRO publish a baseline of training a sequence-to-sequence model with the question and answer, which only achieves 8.9 in terms of ROUGE-L. Adding all passages to the sequence-to-sequence model can obviously improve the result to 28.75. Then we only use the question and the selected passage to genera... |
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension | 1706.04815 | Table 2: The performance on the MS-MARCO test set. *Using the ensemble result of extraction models as the input of the synthesis model. | ['[BOLD] Method', '[BOLD] ROUGE-L', '[BOLD] BLEU-1'] | [['FastQAExt', '33.67', '33.93'], ['Prediction', '37.33', '40.72'], ['ReasoNet', '38.81', '39.86'], ['R-Net', '42.89', '42.22'], ['S-Net (Extraction)', '41.45', '44.08'], ['S-Net (Extraction, Ensemble)', '42.92', '44.97'], ['S-Net', '45.23', '43.78'], ['[BOLD] S-Net*', '[BOLD] 46.65', '[BOLD] 44.78'], ['Human Performan... | Our extraction model achieves 41.45 and 44.08 in terms of ROUGE-L and BLEU-1, respectively. We sum the probability at each position of each single model to decide the ensemble result. Finally we select 13 models for ensemble, which achieves 42.92 and 44.97 in terms of ROUGE-L and BLEU-1, respectively, which achieves th... |
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension | 1706.04815 | Table 3: The performance on the MS-MARCO development set in terms of ROUGE-L. *Using the ensemble result of extraction models as the input of the synthesis model. +Wang & Jiang (2016b) report their Prediction with 37.3. | ['[BOLD] Method', '[BOLD] Extraction', '[BOLD] Extraction [BOLD] +Synthesis'] | [['FastQAExt', '33.7', '-'], ['BiDAF', '34.89', '38.73'], ['Prediction', '37.54+', '41.55'], ['S-Net (w/o Passage Ranking)', '39.62', '43.26'], ['S-Net', '42.23', '45.95'], ['[BOLD] S-Net*', '[BOLD] 44.11', '[BOLD] 47.76']] | Since answers on the test set are not published, we analyze our model on the development set. For the evidence extraction part, our proposed multi-task learning framework achieves 42.23 and 44.11 for the single and ensemble model in terms of ROUGE-L. For the answer synthesis, the single and ensemble models improve 3.72... |
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension | 1706.04815 | Table 4: Results of passage ranking. -w/o Passage Ranking: the model that only has evidence extraction part, without passage ranking part. -Passage Ranking then Extraction: the model that selects the passage firstly and then apply the extraction model only on the selected passage. | ['[BOLD] Method', '[BOLD] P@1', '[BOLD] ROUGE-L'] | [['Extraction w/o Passage Ranking', '34.6', '56.7'], ['Passage Ranking then Extraction', '28.3', '52.9'], ['S-Net (Extraction)', '[BOLD] 38.9', '[BOLD] 59.4']] | We analyze the result of incorporating passage ranking as an additional task. For passage selection, our multi-task model achieves the accuracy of 38.9, which outperforms the pure answer prediction model with 4.3. Moreover, jointly learning the answer prediction part and the passage ranking part is better than solving ... |
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension | 1706.04815 | Table 5: The performance of questions in different levels of necessary of synthesis in terms of ROUGE-L on MS-MARCO development set. | ['[BOLD] Category', '[BOLD] Extraction', '[BOLD] Extraction [BOLD] +Synthesis'] | [['max = 1.0 (63.95%)', '50.74', '49.59'], ['0.8≤max<1.0 (20.06%)', '40.95', '41.16'], ['0.6≤max<0.8 (5.78%)', '31.21', '33.21'], ['0.4≤max<0.6 (1.54%)', '21.97', '22.44'], ['0.2≤max<0.4 (0.29%)', '13.47', '13.49'], ['max<0.2 (8.38%)', '0.01', '49.18']] | For the question whose answer can be exactly matched in the passage, our answer synthesis model performs slightly worse because the sequence-to-sequence model makes some deviation when copying extracted evidences. In other categories, our synthesis model achieves more or less improvement. For the question whose answer ... |
Copy this sentence. | 1905.09856 | Table 1: Best results for each model. BLEU score, seconds per epoch and number of epochs it took to converge to the best BLEU score. | ['Model', 'BLEU', 'Sec/epoch', 'Epochs to converge', 'Num. of parameters'] | [['LSTM', '0.03', '50', '>500', '30,019,166'], ['GRU', '0.74', '93', '16', '57,397,854'], ['CNN', '0.8302', '[BOLD] 30', '50', '49,320,286'], ['Transformer', '[BOLD] 0.8392', '66', '[BOLD] 6', '79,127,134']] | Note that while CNN achieves a similar BLEU score to Transformer, it does so much slower, taking over 50 epochs. Transformer is the clear winner, while taking not much longer to run than other models. |
Neural Cross-Lingual Named Entity Recognition with Minimal Resources | 1808.09861 | Table 2: Comparison of different ways of using bilingual word embeddings, within our method (NER F1). | ['Model', 'Spanish', 'Dutch', 'German'] | [['Common space', '65.40±1.22', '66.15±1.62', '43.73±0.94'], ['Replace', '68.21±1.22', '69.37±1.33', '48.59±1.21'], ['Translation', '[BOLD] 69.21±0.95', '[BOLD] 69.39±1.21', '[BOLD] 53.94±0.66']] | The “common space” variant performs the worst by a large margin, confirming our hypothesis that discrepancy between the two embedding spaces harms the model’s ability to generalize. From the comparison between the “replace” and “translation,” we observe that having access to the target language’s character sequence hel... |
Neural Cross-Lingual Named Entity Recognition with Minimal Resources | 1808.09861 | Table 1: NER F1 scores. ∗Approaches that use more resources than ours (“Wikipedia” means Wikipedia is used not as a monolingual corpus, but to provide external knowledge). †Approaches that use multiple languages for transfer. “Only Eng. data” is the model used in Mayhew et al. (2017) trained on their data translated fr... | ['Model ∗', 'Model Täckström et\xa0al. ( 2012 )', 'Spanish 59.30', 'Dutch 58.40', 'German 40.40', 'Extra Resources parallel corpus'] | [['∗', 'Nothman et\xa0al. ( 2013 )', '61.0', '64.00', '55.80', 'Wikipedia'], ['∗', 'Tsai et\xa0al. ( 2016 )', '60.55', '61.60', '48.10', 'Wikipedia'], ['∗', 'Ni et\xa0al. ( 2017 )', '65.10', '65.40', '58.50', 'Wikipedia, parallel corpus, 5K dict.'], ['∗†', 'Mayhew et\xa0al. ( 2017 )', '65.95', '66.50', '[BOLD] 59.11', ... | Here “BWET” (bilingual word embedding translation) denotes using the hierarchical neural CRF model trained on data translated from English. As can be seen from the table, our methods outperform previous state-of-the-art results on Spanish and Dutch by a large margin and perform competitively on German even without usin... |
Narrative Variations in a Virtual Storyteller | 1708.08585 | Figure 9: Means (M) and standard deviation (SD) for engagement and interest for original sentences and all variations in Perceptions of Voice and POV Experiment | ['Engagement', 'Orig', '1st-out', '1st-neutr', '1st-shy', 'sch', '3rd-neutr'] | [['M', '3.98', '3.27', '3.00', '2.73', '1.95', '1.93'], ['SD', '1.07', '1.39', '1.19', '1.25', '1.07', '1.06'], ['Interest', 'Orig', '1st-out', '1st-neutr', '1st-shy', 'sch', '3rd-neutr'], ['Mean', '3.91', '3.02', '3.02', '2.81', '1.90', '1.87'], ['SD', '0.99', '1.21', '1.37', '1.27', '1.05', '1.01']] | Fig. We find a clear ranking for engagement: the original sentence is scored highest, followed by first outgoing, first neutral, first shy, sch, and third neutral. |
What does a Car-ssette tape tell? | 1905.13448 | Table 4: Results of the baseline model trained on 3 datasets. | ['[EMPTY]', 'BLEU4 Model', 'BLEU4 Human', 'BERT Model', 'BERT Human'] | [['Hospital', '0.127', '0.127', '0.937', '0.942'], ['Car', '0.220', '0.266', '0.919', '0.935'], ['Joint', '0.157', '0.185', '0.925', '0.954']] | In order to verify our model’s generalization capabilities, we trained the baseline model on all 3 datasets. Firstly, our model is capable of being generalized to other datasets, in particular the cross-scene dataset: both the BLEU4 and the BERT similarity score on Joint Dataset are relatively good, meaning that the ba... |
What does a Car-ssette tape tell? | 1905.13448 | Table 2: Token Distribution in DataSet | ['Rank', 'Token', 'Train %', 'Dev %'] | [['1', 'is/are 在', '6.01', '6.01'], ['2', 'driving 行驶', '5.37', '5.55'], ['3', 'automobile 汽车', '5.01', '5.11'], ['4', '‘s 的', '4.01', '4.58'], ['5', 'driver 司机', '3.35', '3.45'], ['mean # of tokens', 'mean # of tokens', '14.21', '14.03']] | The car dataset is split into a training set and a development set, which encompasses 3241 and 361 audio clips respectively. High sentence diversity is observed in both sets: only 6.7% transcriptions in the training set and 1.9% in the development set are repeated. From the distribution of the top 5 tokens in Table it ... |
Siamese CBOW: Optimizing Word Embeddingsfor Sentence Representations | 1606.04640 | Table 3: Time spent per method on all 20 SemEval datasets, 17,608 sentence pairs, and the average time spent on a single sentence pair (time in seconds unless indicated otherwise). | ['[EMPTY]', '20 sets', '1 pair'] | [['Siamese CBOW (300d)', '00,007.7', '0.0004'], ['word2vec (300d)', '00,007.0', '0.0004'], ['skip-thought (1200d)', '98,804.0', '5.6']] | This considerable difference in numbers of arithmetic operations is also observed in practice. We run tests on a single CPU, using identical code for extracting sentences from the evaluation sets, for every method. The sentence pairs are presented one by one to the models. We disregard the time it takes to load models.... |
A Manually Annotated Chinese Corpus forNon-task-oriented Dialogue Systems | 1805.05542 | Table 4: Comparison results (%). Higher scores indicate better results. Cut@N: responses with rating ≥N are considered as positive, and as negative otherwise. Larger cut indicates a stricter standard. Best results in each column is marked as bold. | ['[BOLD] Category', '[BOLD] Models', '[BOLD] Cut@ [BOLD] 3 P@1', '[BOLD] Cut@ [BOLD] 3 MAP', '[BOLD] Cut@ [BOLD] 3 MRR', '[BOLD] Cut@ [BOLD] 4 P@1', '[BOLD] Cut@ [BOLD] 4 MAP', '[BOLD] Cut@ [BOLD] 4 MRR', '[BOLD] Cut@ [BOLD] 5 P@1', '[BOLD] Cut@ [BOLD] 5 MAP', '[BOLD] Cut@ [BOLD] 5 MRR'] | [['[BOLD] Unsupervised', 'Cosine sim', '84.8', '91.1', '91.8', '67.5', '81.3', '82.0', '40.0', '64.9', '65.1'], ['[BOLD] Unsupervised', 'BM25', '86.1', '[BOLD] 91.4', '[BOLD] 92.4', '70.6', '82.7', '83.7', '53.8', '72.3', '73.4'], ['[BOLD] Supervised', 'SVMRank', '[BOLD] 86.2', '91.1', '92.2', '73.0', '[BOLD] 84.0', '8... | We follow the paradigm of question answering to separate responses to be “positive” and “negative” when evaluating the ranked responses given a prompt. In doing so, we set rating thresholds at 3 ,4 and 5, where responses with gold-standard rating ≥N are considered as positive instances, and otherwise as negative instan... |
Multi-stage Pretraining for Abstractive Summarization | 1909.10599 | Table 4: Label coverage of our BERT-based content selection model. We also show the performance of a content selection oracle which always does perfect content selection. True positives here are groundtruth word pieces that are selected by the content selector. The oracle achieves perfect precision because the labels a... | ['CNN/DM dev', 'Oracle', 'Precision 100.00', 'Recall 80.77', 'F1 89.01'] | [['CNN/DM dev', 'Model', '56.11', '46.47', '49.95'], ['CNN/DM test', 'Oracle', '100.00', '80.49', '88.85'], ['CNN/DM test', 'Model', '55.11', '46.58', '49.58']] | Label coverage is an important metric for understanding the performance of content selectors in the context of copy mechanisms. If the content selector has a false negative on a label word, then that word cannot be copied, thus hurting performance. Similarly, if the content selector has a false positive on a label word... |
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping | 1907.05446 | Table 2: Evaluation metrics as percentages on R2R and R4R Validation Unseen sets for agents with different reward functions. In all metrics, higher means better. | ['Agent', '[BOLD] R2R SR', '[BOLD] R2R SPL', '[BOLD] R2R SED', '[BOLD] R2R CLS', '[BOLD] R2R nDTW', '[BOLD] R2R SDTW', '[BOLD] R4R SR', '[BOLD] R4R SPL', '[BOLD] R4R SED', '[BOLD] R4R CLS', '[BOLD] R4R nDTW', '[BOLD] R4R SDTW'] | [['random', '5.1', '3.3', '5.8', '29.0', '27.9', '3.6', '13.7', '2.2', '16.5', '22.3', '18.5', '4.1'], ['goal-oriented', '43.7', '38.4', '31.9', '53.5', '54.4', '36.1', '[BOLD] 28.7', '15.0', '[BOLD] 9.6', '33.4', '26.9', '11.4'], ['fidelity-oriented', '[BOLD] 44.4', '[BOLD] 41.4', '[BOLD] 33.9', '[BOLD] 57.5', '[BOLD]... | Compared to a goal-oriented reward strategy, taking advantage of nDTW as a reward signal not only results in better performance on nDTW and SDTW metrics but also better performance on prior metrics like CLS and SPL. nDTW shows better differentiation compared to CLS on R4R between goal and fidelity oriented agents. SED ... |
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping | 1907.05446 | Table 1: Binomial tests on how different metrics compare in correlation with human judgments. The sign test uses n=sum of positives and negatives; k=number of positives; p=0.5. | ['[EMPTY]', '[BOLD] UC ( [ITALIC] nDTW vs) PL', '[BOLD] UC ( [ITALIC] nDTW vs) NE', '[BOLD] UC ( [ITALIC] nDTW vs) ONE', '[BOLD] UC ( [ITALIC] nDTW vs) CLS', '[BOLD] UC ( [ITALIC] nDTW vs) AD', '[BOLD] UC ( [ITALIC] nDTW vs) MD', '[BOLD] SC ( [ITALIC] SDTW vs) SR', '[BOLD] SC ( [ITALIC] SDTW vs) OSR', '[BOLD] SC ( [ITA... | [['+/-', '242/17', '254/9', '255/9', '162/46', '254/12', '253/12', '219/16', '220/14', '219/17', '213/26'], ['sign test', '4.1e-52', '2.0e-63', '1.0e-63', '2.4e-16', '6.9e-60', '6.9e-60', '9.6e-47', '8.8e-49', '6.7e-46', '1.1e-37']] | To analyze the collected annotations, we first assign nDTW in UC study (similarly SDTW in SC study) a positive/negative sign depending if it has higher/lower correlation than competing metric for a given human ranking of query paths with respect to a reference path, and then compare across all reference paths (discardi... |
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering | 1911.07176 | Table 5: Ablation study, removing different components of ROCC. The scores are reported on the ARC test set and MultiRC dev set. R⋆ denotes the best approach that relies just on the R score. The hyper parameter k in R⋆, was tuned on the development partition of the respective dataset. | ['#', 'Ablations', 'ARC', 'MultiRC EM0', 'MultiRC Justification'] | [['[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', 'F1'], ['0', 'Full AutoROCC', '[BOLD] 56.09', '[BOLD] 25.29', '[BOLD] 56.44'], ['1', '– IDF', '54.11', '24.65', '54.19'], ['2', '– [ITALIC] C( [ITALIC] A)', '54.90', '21.82', '52.93'], ['3', '– [ITALIC] C( [ITALIC] Q)', '54.66', '23.61', '52.09'], ['4', '– O', '55.88', '2... | Row 0 reports the score from the full AutoROCC model. In all the cases, we found small drops in both performance and justification scores across both the datasets, with the removal of either C(A) or C(Q) having the largest impact. |
Revisiting Unsupervised Relation Extraction | 2005.00087 | Table 2: Study of EType+ in combination with different features. The results are average across three runs on the development set. | ['[BOLD] Model EType+', '[BOLD] Model EType+', '[BOLD] B3 42.5', '[BOLD] V 40.1', '[BOLD] ARI 29.2'] | [['[EMPTY]', '+Entity', '40.5', '39.9', '28.6'], ['[EMPTY]', '+BOW', '37.7', '38.0', '20.5'], ['[EMPTY]', '+DepPath', '41.4', '39.4', '26.7'], ['[EMPTY]', '+POS', '41.6', '40.4', '27.8'], ['[EMPTY]', '+Trigger', '41.7', '41.3', '29.0'], ['[EMPTY]', '+PCNN', '40.8', '39.6', '27.1']] | How is the performance when combining entity types with other features? Our experiments using only entity types surprisingly perform higher than the previous state-of-the-art methods including feature engineering and deep learning models. However, we know that context information is crucial to distinguish the relation ... |
Revisiting Unsupervised Relation Extraction | 2005.00087 | Table 1: Average results (%) across three runs of different models (except the EType) on NYT-FB and TACRED. c indicates the number of clusters in each method. ⋄ indicates our implementation of the corresponding model. We note that all methods were trained on NYT-FB and evaluated on the test set of both NYT-FB and TACRE... | ['[BOLD] Model NYT-FB', '[BOLD] Model NYT-FB', '[BOLD] B3 NYT-FB', '[BOLD] V NYT-FB', '[BOLD] ARI NYT-FB'] | [['RelLDA', '[ITALIC] c=10', '29.1', '30.0', '13.3'], ['RelLDA1', '[ITALIC] c=10', '36.9', '34.7', '24.2'], ['March ( [ITALIC] Ls+ [ITALIC] Ld)', '[ITALIC] c=10', '37.5', '38.7', '27.6'], ['Simon', '[ITALIC] c=10', '39.4', '38.3', '[BOLD] 33.8'], ['EType+', '[ITALIC] c=10', '[BOLD] 41.9', '40.6', '30.7'], ['March⋄ ( [I... | Our contributions are as follows: (i) We perform experiments on both automatically/manually-labelled datasets, namely NYT-FB and TACRED, respectively. We show that two methods using only entity types can outperform the state-of-the-art models including both feature-engineering and deep learning approaches. The surprisi... |
Improving Tweet Representations using Temporal and User Context | 1612.06062 | Table 1: User profile attribute classification - F1 Score | ['[BOLD] Algorithm', '[BOLD] Spouse', '[BOLD] Education', '[BOLD] Job'] | [['Paragraph2Vec\xa0', '0.3435', '0.9259', '0.5465'], ['Simple Distance model (SD)', '0.3704', '0.9068', '0.5872'], ['HDV\xa0', '0.4526', '0.8901', '0.521'], ['Ours (User = 0)', '[BOLD] 0.5416', '0.9098', '0.5935'], ['Ours (User = 1)', '0.4082', '[BOLD] 0.9274', '[BOLD] 0.6067']] | HDV’s assumption of giving equal attention value to the temporal context also results in lower accuracy compared with our models. SD model outperforms HDV in two tasks, which substantiates our claim against HDV’s naïve assumption for social media. Our model with user vector outperforming the baselines for Education and... |
The Lifted Matrix-Space Model for Semantic Composition | 1711.03602 | Table 6: Syntactic category classification accuracies (%) on SNLI development set, classified using the tags introduced in Bowman et al. (2015). | ['[BOLD] Model', '[BOLD] 3-way [BOLD] Train', '[BOLD] 3-way [BOLD] Test', '[BOLD] 19-way [BOLD] Train', '[BOLD] 19-way [BOLD] Test'] | [['300D BOW', '86.4', '85.6', '82.7', '82.1'], ['700D TreeLSTM', '93.2', '91.2', '90.0', '86.6'], ['576D LMS-LSTM', '97.3', '[BOLD] 96.3', '94.0', '[BOLD] 92.1']] | As a baseline, we train a bag-of-words (BOW) model which produces the hidden state of a given phrase by summing the GloVe embeddings of the words of the phrase. We train and test on the hidden states produced by BOW as well. The hidden state representations produced by LMS-LSTM yield the best results on both 3-way and ... |
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing | 2004.03849 | Table 7: Comparing Labeled F1 scores of models with different types of embedding combinations on the development set of the gold DM dataset. Baseline represents the parser of wang-etal-2019-second. Base represents the pre-trained BERT-Base uncased model and Large represents the pre-trained BERT-Large uncased model. fix... | ['[EMPTY]', 'LF1'] | [['Baseline', '93.41'], ['Base-fixed', '94.17'], ['Base-tuned', '94.22'], ['Base-fixed + Glove', '94.45'], ['Base-tuned + Glove', '94.48'], ['Large-fixed + Glove', '94.62'], ['Large-tuned + Glove', '94.64'], ['Large-fixed + Glove + Lemma', '95.10'], ['Large-fixed + Glove + Lemma + Char', '95.22'], ['ELMo + Large-fixed ... | We use BERT devlin-etal-2019-bert embedding in our model. We compared the performance of DM in the original SDP dataset with different subtoken pooling methods, and we also explored whether combining other embeddings such as pre-trained word embedding Glove pennington2014glove and contextual embedding ELMo peters-etal-... |
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing | 2004.03849 | Table 1: Comparison of cross-framework F1 scores achieved by our system and best scores of other teams for each metric. all represents the F1 score over the full test set for each framework. lpps represents a 100-sentence sample from the little prince containing graphs over all the frameworks. | ['[EMPTY]', 'DM', 'PSD', 'EDS', 'UCCA', 'AMR'] | [['Ours-all', '94.88', '89.49', '86.90', '-', '63.59'], ['Best-all', '[BOLD] 95.50', '[BOLD] 91.28', '[BOLD] 94.47', '[BOLD] 81.67', '[BOLD] 73.38'], ['Ours-lpps', '94.28', '85.22', '87.49', '-', '66.82'], ['Best-lpps', '[BOLD] 94.96', '[BOLD] 88.46', '[BOLD] 92.82', '[BOLD] 82.61', '[BOLD] 73.11']] | Due to an unexpected bug in UCCA anchor prediction, we failed to submit our UCCA prediction. Our results are still competitive to those of the other teams and we get the \nth3 place for the DM framework in the official metrics. Our system performs well on the DM framework with an F1 score only 0.4 percent F1 below the ... |
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing | 2004.03849 | Table 8: F1 score averaged over the labeled F1 score and the frame F1 score on the development sets of DM and PSD. basic represents our model with embeddings described in 3.1 except lemma and named entity embeddings. | ['[EMPTY]', 'DM', 'PSD'] | [['basic', '96.01', '90.80'], ['+lemma', '96.09', '90.79'], ['+ner', '96.07', '90.80'], ['+lemma & ner', '[BOLD] 96.16', '[BOLD] 90.88']] | We found that one of the difference is about the lemma annotations of entities, for example, lemmas of “Pierre Vinken” are “Pierre” and “Vinken” in the companion data while the lemmas are named-entity-like tags “Pierre” and “_generic_proper_ne” in the original SDP dataset. Based on this discovery, we experimented on th... |
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content | 1808.04911 | Table 4: Top six features correlated with fake news. | ['[BOLD] Feature', '[BOLD] PCC'] | [['unrelated variance', '0.306'], ['distance variance', '0.286'], ['agree variance', '0.280'], ['discuss mean', '-0.231'], ['unrelated mean', '0.210'], ['containsExclamationMark', '0.192']] | To further explore the quality of the cross-lingual cross-platform features, we calculated the Pearson correlation coefficient (PCC) between each feature with respect to the tweet’s label (fake or real). We evaluated both TFG and features used by baseline models. A positive value indicates this feature positively corre... |
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications | 1902.10247 | Table 6: Comparison of different CNN models on the sampled data with graph embeddings | ['Method', 'Negative class (%) precision', 'Negative class (%) recall', 'Negative class (%) F1', 'Positive class (%) precision', 'Positive class (%) recall', 'Positive class (%) F1', 'Overall (%) accuracy', 'Overall (%) F1'] | [['[BOLD] Sampled data', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['CNN-rand', '51.79', '60.42', '55.77', '56.82', '48.08', '52.08', '54.00', '52.08'], ['CNN-static', '64.29', '52.94', '58.06', '58.62', '69.39', '63.55', '61.00', '63.55'], ['CNN-non-static', '54.55', '62.... | We demonstrate the performance of the above described models on the sampled data from all available datasets (250 negative and 250 positive documents divided into train and test on 80-20 ratio) to figure out which model is the best choice to be coupled with graph embeddings. The results reveal that the CNN-static model... |
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