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Sparse Sequence-to-Sequence Models | 1905.05702 | Table 1: Average per-language accuracy on the test set (CoNLL–SIGMORPHON 2018 task 1) averaged or ensembled over three runs. | ['[ITALIC] α output', '[ITALIC] α attention', 'high (avg.)', 'high (ens.)', 'medium (avg.)', 'medium (ens.)'] | [['1', '1', '93.15', '94.20', '82.55', '85.68'], ['[EMPTY]', '1.5', '92.32', '93.50', '83.20', '85.63'], ['[EMPTY]', '2', '90.98', '92.60', '83.13', '85.65'], ['1.5', '1', '94.36', '94.96', '84.88', '86.38'], ['[EMPTY]', '1.5', '94.44', '95.00', '84.93', '86.55'], ['[EMPTY]', '2', '94.05', '94.74', '84.93', '86.59'], [... | We report the official metric of the shared task, word accuracy averaged across languages. In addition to the average results of three individual model runs, we use an ensemble of those models, where we decode by averaging the raw probabilities at each time step. Our best sparse loss models beat the softmax baseline by... |
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach | 1908.05854 | Table 1: Evaluation results. Marked with asterisks are individual results higher than the ZSDG baseline which are achieved with the minimum amount of training data, and in bold is the model consistently outperforming ZSDG in all domains and metrics with minimum data. | ['[BOLD] ModelDomain', '[BOLD] Navigation BLEU, %', '[BOLD] Navigation Entity F1, %', '[BOLD] Weather BLEU, %', '[BOLD] Weather Entity F1, %', '[BOLD] Schedule BLEU, %', '[BOLD] Schedule Entity F1, %'] | [['ZSDG', '5.9', '14.0', '8.1', '31', '7.9', '36.9'], ['NLU_ZSDG', '6.1±2.2', '12.7±3.3', '5.0±1.6', '16.8±6.7', '6.0±1.7', '26.5±5.4'], ['NLU_ZSDG+LAED', '7.9±1', '12.3±2.9', '8.7±0.6', '21.5±6.2', '8.3±1', '20.7±4.8'], ['FSDG@1%', '6.0±1.8', '9.8±4.8', '6.9±1.1', '22.2±10.7', '5.5±0.8', '25.6±8.2'], ['FSDG@3%', '7.9±... | Our objective here is maximum accuracy with minimum training data required, and it can be seen that few-shot models with LAED representation are the best performing models for this objective. While the improvements can already be seen with simple FSDG, the use of LAED representation helps to significantly reduce the am... |
A Hierarchical Model for Data-to-Text Generation | 1912.10011 | Table 1: Evaluation on the RotoWire testset using relation generation (RG) count (#) and precision (P%), content selection (CS) precision (P%) and recall (R%), content ordering (CO), and BLEU. -: number of parameters unavailable. | ['[EMPTY]', 'BLEU', 'RG P%', 'RG #', 'CS P%', 'CS R%', 'CS F1', 'CO', 'Nb Params'] | [['Gold descriptions', '100', '96.11', '17.31', '100', '100', '100', '100', '[EMPTY]'], ['Wiseman', '14.5', '75.62', '[BOLD] 16.83', '32.80', '39.93', '36.2', '15.62', '45M'], ['Li', '16.19', '84.86', '19.31', '30.81', '38.79', '34.34', '16.34', '-'], ['Pudupully-plan', '16.5', '87.47', '34.28', '34.18', '51.22', '41',... | For each proposed variant of our architecture, we report the mean score over ten runs, as well as the standard deviation in subscript. We also report the result of the oracle (metrics on the gold descriptions). Please note that gold descriptions trivially obtain 100% on all metrics expect RG, as they are all based on c... |
MOSS: End-to-End Dialog System Framework with Modular Supervision | 1909.05528 | Table 1: Performance comparison on CamRest676 among the baselines, MOSS-all, and several variants of MOSS. | ['[BOLD] Model', '[BOLD] Mat', '[BOLD] Succ.F1', '[BOLD] BLEU'] | [['KVRN', '[EMPTY]', '[EMPTY]', '0.134'], ['NDM', '0.904', '0.832', '0.212'], ['LIDM', '0.912', '0.840', '0.246'], ['TSCP', '0.927', '0.854', '0.253'], ['MOSS w/o DPL', '0.932', '0.856', '0.251'], ['MOSS w/o NLU', '0.932', '0.857', '0.255'], ['MOSS-all × 60%', '0.947', '0.857', '0.202'], ['MOSS × (60%all + 40%raw )', '... | The first key takeaway is that the more supervision the model has, the better the performance is. (i) KVRN < (ii) NDM ≈ (iii) LIDM < (iv) TSCP < (v) MOSS w/o DPL ≈ (vi) MOSS w/o NLU < (ix) MOSS-all. We note that this performance ranking is the same as the ranking of how much supervision each system receives: (i) KVRN o... |
An Automated Text Categorization Framework based on Hyperparameter Optimization | 1704.01975 | Table 2: Authorship Attribution Data sets. | ['Dataset', '[BOLD] macro-F1 Cummins\xa0', '[BOLD] macro-F1 Escalante ', '[BOLD] macro-F1 [ITALIC] μTC'] | [['CCA', '0.0182', '0.7032', '[BOLD] 0.7633'], ['NFL', '[BOLD] 0.7654', '0.7637', '0.7422'], ['Business', '0.7548', '0.7808', '[BOLD] 0.8199'], ['Poetry', '0.4489', '0.7003', '[BOLD] 0.7135'], ['Travel', '0.6758', '0.7392', '[BOLD] 0.8621'], ['Cricket', '0.9170', '0.8810', '[BOLD] 0.9665']] | The first task analyzed is authorship attribution, The pre-processing stage of the μTC’s input is all-terms; others use the stemmed stage. The best performing classifiers are created by μTC, except for NFL where alternatives perform better. In the case of Business, Escalante et. Please notice that NFL and Bussiness are... |
An Automated Text Categorization Framework based on Hyperparameter Optimization | 1704.01975 | Table 2: Authorship Attribution Data sets. | ['Dataset', '[BOLD] Accuracy Cummins\xa0', '[BOLD] Accuracy Escalante ', '[BOLD] Accuracy [ITALIC] μTC'] | [['CCA', '0.1000', '0.7372', '[BOLD] 0.7660'], ['NFL', '0.7778', '[BOLD] 0.8376', '0.7555'], ['Business', '0.7556', '[BOLD] 0.8358', '0.8222'], ['Poetry', '0.5636', '0.7405', '[BOLD] 0.7272'], ['Travel', '0.6833', '0.7845', '[BOLD] 0.8667'], ['Cricket', '0.9167', '0.9206', '[BOLD] 0.9667']] | The first task analyzed is authorship attribution, The pre-processing stage of the μTC’s input is all-terms; others use the stemmed stage. The best performing classifiers are created by μTC, except for NFL where alternatives perform better. In the case of Business, Escalante et. Please notice that NFL and Bussiness are... |
An Automated Text Categorization Framework based on Hyperparameter Optimization | 1704.01975 | Table 4: Author profiling: PAN2017 benchmark [21], all methods were scored with the official gold-standard. All scores are based on the accuracy computation over the specified subset of items. | ['Method', 'Task', 'Arabic', 'English', 'Spanish', 'Portuguese', 'Avg.'] | [['[EMPTY]', 'Gender', '0.7569', '0.7938', '0.7975', '0.8038', '0.7880'], ['[ITALIC] μTC', 'Variety', '0.7894', '0.8388', '0.9364', '0.9750', '0.8849'], ['[EMPTY]', 'Joint', '0.6081', '0.6704', '0.7518', '0.7850', '0.7038'], ['[EMPTY]', 'Gender', '0.8006', '0.8233', '0.8321', '0.8450', '[BOLD] 0.8253'], ['Basile et al.... | Please note that the result by Tellez et al. The plain μTC, as described in this manuscript, achieves accuracies of 0.7880 and 0.8849, respectively for gender and variety identification. The joint prediction of both classes achieves an accuracy of 0.7038. |
An Automated Text Categorization Framework based on Hyperparameter Optimization | 1704.01975 | Table 7: spam classification | ['Data set', '[BOLD] macro-F1 Androutsopoulos ', '[BOLD] macro-F1 Sakkis ', '[BOLD] macro-F1 Cheng ', '[BOLD] macro-F1 [ITALIC] μTC'] | [['Ling-Spam', '-', '0.8957', '0.9870', '[BOLD] 0.9979'], ['PUA', '0.8897', '-', '-', '[BOLD] 0.9478'], ['PU1', '0.9149', '-', '[BOLD] 0.983', '0.9664'], ['PU2', '0.6794', '-', '-', '[BOLD] 0.9044'], ['PU3', '0.9265', '-', '[BOLD] 0.977', '0.9701']] | Here, it can be seen that best results in the macro-F1 measure were obtained with our approach μTC; nevertheless, the best results in the accuracy score were achieved by Androutsopoulos et al. |
Impact of ASR on Alzheimer’s Disease Detection:All Errors are Equal, but Deletions are More Equal than Others | 1904.01684 | Table 1: Rates of ASR errors on DB and FP datasets. | ['[BOLD] Dataset DB', '[BOLD] Dataset HC', '[BOLD] Del (%) 52.55', '[BOLD] Ins (%) 4.15', '[BOLD] Sub (%) 43.30'] | [['DB', 'AD', '42.99', '5.70', '51.31'], ['[0.5pt/2pt] FP', 'HC', '34.16', '11.72', '54.12'], ['[0.5pt/2pt] FP', 'AD', '29.70', '12.14', '58.16']] | Rates of ASR errors for healthy and impaired speakers for DB and FP datasets are in Tab. Majority of errors arise from deletions and substitutions for both datasets and for both groups. |
Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage HPSG: A Case Study on Machine Translation | 1809.02035 | Table 1: The distribution of root node conditions for the reference and NMT translations on the 200K analysis sentence pairs. Root node conditions are taken from the recorded best derivation. The best derivation is chosen by the maximum entropy model included in the ERG. | ['[BOLD] Source', '[BOLD] Strict [BOLD] Full', '[BOLD] Strict [BOLD] Frag', '[BOLD] Informal [BOLD] Full', '[BOLD] Informal [BOLD] Frag', '[BOLD] Unpar- [BOLD] seable'] | [['Ref', '64.7', '2.4', '31.5', '1.4', '0.0'], ['NMT', '60.5', '3.0', '28.1', '1.6', '6.8'], ['Δ', '-4.2', '+0.6', '-3.4', '+0.2', '+6.8']] | Root node conditions are used by the ERG to denote whether the parser had to relax punctuation and capitalization rules, with “strict” and “informal”, and whether the derivation is of a full sentence or a fragment, with “full” and “frag”. Fragments can be isolated noun, verb, or prepositional phrases. Both full sentenc... |
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval | 1908.02425 | Table 4. Accuracy, precision, recall, and F1-score for policy agenda classification across 31 policy documents in Malawi (n=12), Kenya (n=12), and Rwanda (n=7). | ['Agenda', 'Accuracy', 'Precision', 'Recall', 'F1'] | [['Maintaining # trees', '0.90', '1.00', '0.90', '0.97'], ['Increasing # trees', '0.71', '0.75', '0.78', '0.77'], ['Economic benefits', '0.84', '0.89', '0.84', '0.86'], ['Health benefits', '0.74', '0.88', '0.77', '0.82'], ['Benefit sharing', '0.88', '0.92', '0.55', '0.75'], ['Land ownership', '0.87', '0.83', '0.89', '0... | The methodology performed best on topics with a very narrow scope and little overlap with other agenda, such as the establishment of buffer zones, and worst on topics that are broad in nature, subjective, and have potential to overlap with other agenda, such as land use rights and forest protection. Overall, we report ... |
Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages | 1704.08914 | Table 2: MRR results for step 4. See text for details. | ['language', 'past', 'present', 'future', 'all'] | [['Arabic', '1.00', '0.39', '0.77', '0.72'], ['Chinese', '0.00', '0.00', '0.87', '0.29'], ['English', '1.00', '1.00', '1.00', '1.00'], ['French', '1.00', '1.00', '1.00', '1.00'], ['German', '1.00', '1.00', '1.00', '1.00'], ['Italian', '1.00', '1.00', '1.00', '1.00'], ['Persian', '0.77', '1.00', '1.00', '0.92'], ['Polis... | We make three contributions. (i) Our basic hypotheses are H1 and H2. (H1) For an important linguistic feature, there exist a few languages that mark it overtly and easily recognizably. (H2) It is possible to project overt markers to overt and non-overt markers in other languages. Based on these two hypotheses we design... |
Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors | 1907.06111 | TABLE III: Combinations of Uncertainty Normalization, Regularized LDA, S-Norm and PLDA | ['Model', 'Version', '[BOLD] Male EER [%]', '[BOLD] Male NDCFminold', '[BOLD] Male NDCFminnew', '[BOLD] Female EER [%]', '[BOLD] Female NDCFminold', '[BOLD] Female NDCFminnew'] | [['Proposed', 'Uncert. Norm, Reg. LDA, S-Norm', '[BOLD] 1.52', '[BOLD] 0.093', '[BOLD] 0.517', '[BOLD] 1.77', '[BOLD] 0.094', '[BOLD] 0.424'], ['Proposed', 'Uncert. Norm, Reg. LDA', '2.04', '0.113', '0.533', '2.57', '0.133', '0.515'], ['Proposed', 'S-Norm', '2.15', '0.113', '0.546', '3.12', '0.143', '0.561'], ['Propose... | First of all, we observe that the contribution of Regularized LDA is rather minor compared to uncertainty normalization. This result is rather surprising; it shows that state-of-the-art performance can be attained even without explicit channel modelling, i.e. without the need of collecting multiple training recording c... |
Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors | 1907.06111 | TABLE V: Comparison between different number of HMM states. | ['Number of HMM states', '[BOLD] Male EER [%]', '[BOLD] Male NDCFminold', '[BOLD] Male NDCFminnew', '[BOLD] Female EER [%]', '[BOLD] Female NDCFminold', '[BOLD] Female NDCFminnew'] | [['4', '1.59', '0.096', '0.519', '1.82', '0.097', '0.431'], ['8', '1.52', '0.093', '0.517', '1.77', '0.094', '0.424'], ['16', '1.50', '0.089', '0.516', '1.76', '0.089', '0.422'], ['32', '1.56', '0.094', '0.520', '1.78', '0.092', '0.428']] | As we observe, the performance is rather insensitive to Sd, being slightly higher for Sd=16. However, we choose to use Sd=8 for the rest of the experiments, since their differences are minor and the algorithm becomes less computationally and memory demanding. |
Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors | 1907.06111 | TABLE VI: Comparison between LDA with and without length normalization. | ['Method', '[BOLD] Male EER [%]', '[BOLD] Male NDCFminold', '[BOLD] Male NDCFminnew', '[BOLD] Female EER [%]', '[BOLD] Female NDCFminold', '[BOLD] Female NDCFminnew'] | [['LDA with length normalization', '1.52', '0.093', '0.517', '1.77', '0.094', '0.424'], ['LDA without length normalization', '1.68', '0.112', '0.521', '1.98', '0.105', '0.431']] | It is generally agreed that applying length normalization before LDA improves its performance. In order to reexamine its positive effect we perform an experiment to compare the performance of length normalization followed by LDA and LDA without length normalization. The system with MFCC features and uncertainty normali... |
Neural Emoji Recommendation in Dialogue Systems | 1612.04609 | Table 3: Evaluation results of P@1 on different emojis | ['Emoji', 'Definition', 'S-LSTM', 'H-LSTM'] | [['[EMPTY]', '[ITALIC] tears of joy', '16.5', '21.6'], ['[EMPTY]', '[ITALIC] thinking', '21.6', '22.7'], ['[EMPTY]', '[ITALIC] laugh', '17.5', '24.1'], ['[EMPTY]', '[ITALIC] nervous', '23.2', '27.1'], ['[EMPTY]', '[ITALIC] shy', '23.5', '28.5'], ['[EMPTY]', '[ITALIC] delicious', '33.1', '32.7'], ['[EMPTY]', '[ITALIC] c... | For further comparisons, we report the P@1 results on different emoji categories. The evaluation results on different emojis show different performances, which implies that there are indeed existing emojis that are more confusing and harder to be predicted than other emojis. (2) Emojis such as heart and angry are relat... |
Unsupervised Recurrent Neural Network Grammars | 1904.03746 | Table 1: Language modeling perplexity (PPL) and grammar induction F1 scores on English (PTB) and Chinese (CTB) for the different models. We separate results for those that make do not make use of annotated data (top) versus those that do (mid). Note that our PTB setup from Dyer et al. (2016) differs considerably from t... | ['Model', 'PTB PPL', 'PTB [ITALIC] F1', 'CTB PPL', 'CTB [ITALIC] F1'] | [['RNNLM', '93.2', '–', '201.3', '–'], ['PRPN (default)', '126.2', '32.9', '290.9', '32.9'], ['PRPN (tuned)', '96.7', '41.2', '216.0', '36.1'], ['Left Branching Trees', '100.9', '10.3', '223.6', '12.4'], ['Right Branching Trees', '93.3', '34.8', '203.5', '20.6'], ['Random Trees', '113.2', '17.0', '209.1', '17.4'], ['UR... | As a language model URNNG outperforms an RNNLM and is competitive with the supervised RNNG. The left branching baseline performs poorly, implying that the strong performance of URNNG/RNNG is not simply due to the additional depth afforded by the tree LSTM composition function (a left branching tree, which always perfor... |
Unsupervised Recurrent Neural Network Grammars | 1904.03746 | Table 2: (Top) Comparison of this work as a language model against prior works on sentence-level PTB with preprocessing from Dyer et al. (2016). Note that previous versions of RNNG differ from ours in terms of parameterization and model size. (Bottom) Results on a subset (1M sentences) of the one billion word corpus. P... | ['PTB', 'PPL'] | [['KN 5-gram Dyer et\xa0al. ( 2016 )', '169.3'], ['RNNLM Dyer et\xa0al. ( 2016 )', '113.4'], ['Original RNNG Dyer et\xa0al. ( 2016 )', '102.4'], ['Stack-only RNNG Kuncoro et\xa0al. ( 2017 )', '101.2'], ['Gated-Attention RNNG Kuncoro et\xa0al. ( 2017 )', '100.9'], ['Generative Dep. Parser Buys and Blunsom ( 2015 )', '13... | We find that a standard language model (RNNLM) is better at modeling short sentences, but underperforms models that explicitly take into account structure (RNNG/URNNG) when the sentence length is greater than 10. On this larger dataset URNNG still improves upon the RNNLM. We also trained an RNNG (and RNNG → URNNG) on t... |
Unsupervised Recurrent Neural Network Grammars | 1904.03746 | Table 5: Metrics related to the generative model/inference network for RNNG/URNNG. For the supervised RNNG we take the “inference network” to be the discriminative parser trained alongside the generative model (see section 3.3). Recon. PPL is the reconstruction perplexity based on Eqϕ(z|x)[logpθ(x|z)], and KL is the Ku... | ['[EMPTY]', 'PTB RNNG', 'PTB URNNG', 'CTB RNNG', 'CTB URNNG'] | [['PPL', '88.7', '90.6', '193.1', '195.7'], ['Recon. PPL', '74.6', '73.4', '183.4', '151.9'], ['KL', '7.10', '6.13', '11.11', '8.91'], ['Prior Entropy', '7.65', '9.61', '9.48', '15.13'], ['Post. Entropy', '1.56', '2.28', '6.23', '5.75'], ['Unif. Entropy', '26.07', '26.07', '30.17', '30.17']] | The “reconstruction” perplexity based on Eqϕ(z|x)[logpθ(x|z)] is much lower than regular perplexity, and further, the Kullback-Leibler divergence between the conditional prior and the variational posterior, given by Eqϕ(z|x)[logqϕ(z|x)pθ(z|x |
SciDTB: Discourse Dependency TreeBank for Scientific Abstracts | 1806.03653 | Table 5: Performance of baseline parsers. | ['[EMPTY]', '[BOLD] Dev set [BOLD] UAS', '[BOLD] Dev set [BOLD] LAS', '[BOLD] Test set [BOLD] UAS', '[BOLD] Test set [BOLD] LAS'] | [['Vanilla transition', '[BOLD] 0.730', '0.557', '[BOLD] 0.702', '0.535'], ['Two-stage transition', '[BOLD] 0.730', '[BOLD] 0.577', '[BOLD] 0.702', '[BOLD] 0.545'], ['Graph-based', '0.607', '0.455', '0.576', '0.425'], ['Human', '0.806', '0.627', '0.802', '0.622']] | We also measure parsing accuracy with UAS and LAS. The human agreement is presented for comparison. With the addition of tree structural features in relation type prediction, the two-stage dependency parser gets better performance on LAS than vanilla system on both development and test set. Compared with graph-based mo... |
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion | 1708.09085 | Table 6: Means and σ for belief change for neutral and entrenched participants presented with mono, fact, or emot argument types. Neutrals show more belief change, and all argument types significantly affect beliefs | ['[EMPTY]', '[BOLD] N', '[BOLD] Mean change', '[ITALIC] σ [BOLD] change'] | [['mono entrenched', '1826', '0.50', '1.09'], ['mono neutral', '1359', '0.62', '0.71'], ['fact entrenched', '258', '0.27', '0.79'], ['fact neutral', '202', '0.39', '0.55'], ['emot entrenched', '213', '0.35', '0.87'], ['emot neutral', '187', '0.37', '0.54'], ['ALL entrenched', '2951', '0.43', '1.00'], ['ALL neutral', '2... | Our first question is whether our method changed participant’s beliefs. Belief change occurred for all argument types: and the change was statistically significant as measured by paired t-tests (t(5184) = 38.31, p <0.0001). This confirms our hypothesis that social media can be mined for persuasive materials. In additio... |
Content based Weighted Consensus Summarization | 1802.00946 | Table 1: System performance comparison | ['System', 'DUC 2003 R-1', 'DUC 2003 R-2', 'DUC 2003 R-4', 'DUC 2004 R-1', 'DUC 2004 R-2', 'DUC 2004 R-4'] | [['LexRank', '0.357', '0.081', '0.009', '0.354', '0.075', '0.009'], ['TexRank', '0.353', '0.072', '0.010', '0.356', '0.078', '0.010'], ['Centroid', '0.330', '0.067', '0.008', '0.332', '0.059', '0.005'], ['FreqSum', '0.349', '0.080', '0.010', '0.347', '0.082', '0.010'], ['TsSum', '0.344', '0.750', '0.008', '0.352', '0.0... | The DUC 2003 and DUC 2004 datasets were used for evaluating the experiments. We report ROUGE-1, ROUGE-2 and ROUGE-4 recall. We use three baseline aggregation techniques against which the proposed method is compared. Besides Borda Count and WCS, we also compare the results with the choose-best Oracle technique. In case ... |
A Unified Tagging Solution:Bidirectional LSTM Recurrent Neural Network with Word Embedding | 1511.00215 | Table 5: Comparison different word embeddings. | ['[BOLD] WE', '[BOLD] Dim', '[BOLD] Vocab#', '[BOLD] Train Corpus (Toks #)', '[BOLD] POS (Acc)', '[BOLD] CHUNK (F1)', '[BOLD] NER'] | [['[BOLD] ', '80', '82K', 'Broadcast news (400M)', '96.97', '92.53', '84.69'], ['[BOLD] ', '50', '130K', 'RCV1+Wiki (221M+631M)', '97.02', '93.76', '89.34'], ['[BOLD] ', '300', '3M', 'Google news (10B)', '96.85', '92.45', '85.80'], ['[BOLD] ', '100', '1193K', 'Twitter (27B)', '97.02', '93.01', '87.33'], ['[BOLD] BLSTMW... | 1 news set. RANDOM is the word embedding set composed of random values which is the baseline. BSLTMWE(10m), BSLTMWE(100m) and BSLTMWE(all) are word embeddings respectively trained by BLSTM-RNN on the first 10 million words, first 100 million words and all 536 million words of North American news corpus. While BSLTMWE(1... |
A Unified Tagging Solution:Bidirectional LSTM Recurrent Neural Network with Word Embedding | 1511.00215 | Table 4: Comparison of systems with one and two BLSTM hidden layers. | ['[BOLD] Sys', '[BOLD] POS(Acc)', '[BOLD] CHUNK(F1)', '[BOLD] NER(F1)'] | [['B', '96.60', '91.91', '82.52'], ['BB', '96.63', '91.76', '82.66']] | Besides, we also evaluate deep structure which uses multiple BLSTM layers. This deep BLSTM has been reported achieving significantly better performance than single layer BLSTM in various applications such as speech synthesis Size of all hidden layers is set 100. |
Cross-domain Semantic Parsing via Paraphrasing | 1704.05974 | Table 3: Main experiment results. We combine the proposed paraphrase model with different word embedding initializations. I: in-domain, X: cross-domain, EN: per-example normalization, FS: per-feature standardization, ES: per-example standardization. | ['Method', 'Calendar', 'Blocks', 'Housing', 'Restaurants', 'Publications', 'Recipes', 'Social', 'Basketball', 'Avg.'] | [['[BOLD] Previous Methods', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Wang et al.\xa0Wang et\xa0al. ( 2015 )', '74.4', '41.9', '54.0', '75.9', '59.0', '70.8', '48.2', '46.3', '58.8'], ['Xiao et al.\xa0Xiao et\xa0al. ( 2016 )', '75.0', '55.6', '61.9', '80.1', ... | 5.3.1 Comparison with Previous Methods With our main novelties, cross-domain training and word embedding standardization, our full model is able to outperform the previous best model, and achieve the best accuracy on 6 out of the 8 domains. Next we examine the novelties separately. |
Grammatical Error Correction with Neural Reinforcement Learning | 1707.00299 | Table 4: Human (TrueSkill) and GLEU evaluation of system outputs on the development and test set. | ['Models', 'dev set Human', 'dev set GLEU', 'test set Human', 'test set GLEU'] | [['Original', '-1.072', '38.21', '-0.760', '40.54'], ['AMU', '-0.405', '41.74', '-0.168', '44.85'], ['CAMB14', '-0.160', '42.81', '-0.225', '46.04'], ['NUS', '-0.131', '46.27', '-0.249', '50.13'], ['CAMB16', '-0.117', '47.20', '-0.164', '52.05'], ['MLE', '-0.052', '48.24', '-0.110', '52.75'], ['NRL', '0.169', '[BOLD] 4... | In both dev and test set, NRL outperforms MLE and other baselines in both the human and automatic evaluations. Human evaluation and GLEU scores correlate highly, corroborating the reliability of GLEU. With respect to inter-annotator agreement, Spearman’s rank correlation between Turkers is 55.6 for the dev set and 49.2... |
Explainable Rumor Detection using Inter and Intra-feature Attention Networks | 2007.11057 | Table 5: Attention values for content latent features (USE embedding), handcrafted content features and user features for three records numbered 857, 870 and 94. Label 0 corresponds to a false rumor and Label 1 corresponds to a true rumor. In all cases, the model identified the status of the tweet correctly. | ['[BOLD] Record Number', '857', '870', '94'] | [['[BOLD] Label', '0', '1', '1'], ['[BOLD] Latent Features', '0.26', '0.41', '0.308'], ['[BOLD] Handcrafted Features', '0.363', '0.38', '0.447'], ['[BOLD] User Features', '0.377', '0.21', '0.245']] | Finally we show how to interpret the relative importance between the feature classes themselves (user features, handcrafted features and latent features) using an example. For record number 94, (identified correctly as the true rumor) the model gave 30.8% of the weight to latent features, 44.7% weight to handcrafted co... |
REST: A Thread Embedding Approach for Identifying and Classifying User-specified Information in Security Forums | 2001.02660 | Table 11: Classification: the performance of the five different methods in classifying threads in 10-fold cross validation. | ['Datasets', 'Metrics', 'BOW', 'NMF', 'SWEM', 'FastText', 'LEAM', 'BERT', '[BOLD] REST'] | [['OffensComm.', 'Accuracy', '75.33±0.1', '74.31±0.1', '75.55±0.21', '74.64±0.15', '74.88±0.22', '[BOLD] 78.58± 0.08', '77.1±0.18'], ['OffensComm.', 'F1 Score', '[EMPTY]', '[EMPTY]', '74.15±0.23', '72.5±0.15', '72.91±0.18', '[BOLD] 78.47±0.01', '75.10±0.14'], ['HackThisSite', 'Accuracy', '65.3±0.41', '69.46±0.12', '73.... | REST compared to the state-of-the-art. Our approach compares favourably against the competition. REST outperforms other baseline methods with at least 1.4 percentage point in accuracy and 0.7 percentage point in F1 score, except BERT. First, using BERT “right out of the box” did not give good results initially. However... |
REST: A Thread Embedding Approach for Identifying and Classifying User-specified Information in Security Forums | 2001.02660 | Table 8: Identification Precision: the precision of the identified thread of interest with the similarity-based method. | ['[EMPTY]', 'OffensComm.', 'HackThisSite', 'EthicHack', 'Avg.'] | [['Precision', '98.2', '97.5', '97.0', '97.5']] | Estimating precision. To evaluate precision, we want to identify what percentage of the retrieved threads are relevant. To this end, we resort to manual evaluation. We have labeled 300 threads from each dataset retrieved with 50% of the keywords and we get our annotators to identify if they are relevant. We understand ... |
Gender Bias in Coreference Resolution:Evaluation and Debiasing Methods | 1804.06876 | Table 2: F1 on OntoNotes and WinoBias development set. WinoBias results are split between Type-1 and Type-2 and in pro/anti-stereotypical conditions. * indicates the difference between pro/anti stereotypical conditions is significant (p | ['Method E2E', 'Anon.', 'Resour.', 'Aug.', 'OntoNotes [BOLD] 67.7', 'T1-p [BOLD] 76.0', 'T1-a 49.4', 'Avg 62.7', '∣ Diff ∣ 26.6*', 'T2-p [BOLD] 88.7', 'T2-a 75.2', 'Avg 82.0', '∣ Diff ∣ 13.5*'] | [['E2E', '[EMPTY]', '[EMPTY]', '[EMPTY]', '66.4', '73.5', '51.2', '62.6', '21.3*', '86.3', '70.3', '78.3', '16.1*'], ['E2E', '[EMPTY]', '[EMPTY]', '[EMPTY]', '66.5', '67.2', '59.3', '63.2', '7.9*', '81.4', '82.3', '81.9', '0.9'], ['E2E', '[EMPTY]', '[EMPTY]', '[EMPTY]', '66.2', '65.1', '59.2', '62.2', '5.9*', '86.5', '... | WinoBias Reveals Gender Bias Table Systems were evaluated on both types of sentences in WinoBias (T1 and T2), separately in pro-stereotyped and anti-stereotyped conditions ( T1-p vs. T1-a, T2-p vs T2-a). E2E and Feature were retrained in each condition using default hyper-parameters while Rule was not debiased because ... |
Question Answering as an Automatic Evaluation Metric for News Article Summarization | 1906.00318 | Table 2: Correlation matrix of ROUGE and APES. | ['[EMPTY]', 'R-1', 'R-2', 'R-L', 'R-SU', 'APES'] | [['R-1', '1.00', '0.83', '0.92', '0.94', '0.66'], ['R-2', '[EMPTY]', '1.00', '0.82', '0.90', '0.61'], ['R-L', '[EMPTY]', '[EMPTY]', '1.00', '0.89', '0.66'], ['R-SU', '[EMPTY]', '[EMPTY]', '[EMPTY]', '1.00', '0.67'], ['APES', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '1.00']] | While ROUGE baselines were beaten only by a very small number of suggested metrics in the original AESOP task, we find that APES shows better correlation than the popular R-1, R-2 and R-L, and the strong R-SU. Although showing statistical significance for our hypothesis is difficult because of the small dataset size, w... |
Question Answering as an Automatic Evaluation Metric for News Article Summarization | 1906.00318 | Table 1: Pearson Correlation of ROUGE and APES against Pyramid and Responsiveness on summary level. Statistically significant differences are marked with *. | ['[EMPTY]', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L', 'ROUGE-SU', 'APES'] | [['Pyramid', '0.590', '0.468*', '0.599', '0.563*', '[BOLD] 0.608'], ['Responsiveness', '0.540', '0.518*', '0.537', '0.541', '[BOLD] 0.576']] | We follow the work of Louis and Nenkova and compare input level APES scores with manual Pyramid and Responsiveness scores provided in the AESOP task. In Input level, correlation is computed for each summary against its manual score. In contrast, system level reports the average score for a summarization system over the... |
Efficient Neural Architecture Search via Parameter Sharing | 1802.03268 | Table 2: Classification errors of ENAS and baselines on CIFAR-10. In this table, the first block presents DenseNet, one of the state-of-the-art architectures designed by human experts. The second block presents approaches that design the entire network. The last block presents techniques that design modular cells which... | ['[BOLD] Method', '[BOLD] GPUs', '[BOLD] Times', '[BOLD] Params', '[BOLD] Error'] | [['[BOLD] Method', '[BOLD] GPUs', '(days)', '(million)', '(%)'], ['DenseNet-BC\xa0(Huang et\xa0al., 2016 )', '−', '−', '25.6', '3.46'], ['DenseNet + Shake-Shake\xa0(Gastaldi, 2016 )', '−', '−', '26.2', '2.86'], ['DenseNet + CutOut\xa0(DeVries & Taylor, 2017 )', '−', '−', '26.2', '[BOLD] 2.56'], ['Budgeted Super Nets... | S3SS2SSS0Px4 Results. If we keep the architecture, but increase the number of filters in the network’s highest layer to 512, then the test error decreases to 3.87%, which is not far away from NAS’s best model, whose test error is 3.65%. Impressively, ENAS takes about 7 hours to find this architecture, reducing the numb... |
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification | 1805.02220 | Table 3: Performance of our method and competing models on the MS-MARCO test set | ['Model', 'ROUGE-L', 'BLEU-1'] | [['FastQA_Ext Weissenborn et\xa0al. ( 2017 )', '33.67', '33.93'], ['Prediction Wang and Jiang ( 2016 )', '37.33', '40.72'], ['ReasoNet Shen et\xa0al. ( 2017 )', '38.81', '39.86'], ['R-Net Wang et\xa0al. ( 2017c )', '42.89', '42.22'], ['S-Net Tan et\xa0al. ( 2017 )', '45.23', '43.78'], ['Our Model', '[BOLD] 46.15', '[BO... | et al. As we can see, for both metrics, our single model outperforms all the other competing models with an evident margin, which is extremely hard considering the near-human performance. If we ensemble the models trained with different random seeds and hyper-parameters, the results can be further improved and outperfo... |
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification | 1805.02220 | Table 4: Performance on the DuReader test set | ['Model', 'BLEU-4', 'ROUGE-L'] | [['Match-LSTM', '31.8', '39.0'], ['BiDAF', '31.9', '39.2'], ['PR + BiDAF', '37.55', '41.81'], ['Our Model', '[BOLD] 40.97', '[BOLD] 44.18'], ['Human', '56.1', '57.4']] | The BiDAF and Match-LSTM models are provided as two baseline systems He et al. We can see that this paragraph ranking can boost the BiDAF baseline significantly. Finally, we implement our system based on this new strategy, and our system (single model) achieves further improvement by a large margin. |
Contextual Lensing of Universal Sentence Representations | 2002.08866 | Table 5: F1 scores for parallel data mining on the BUCC 2018 training and test sets. The first four language columns are training while the last four are testing. Middle group are unsupervised mining. For training data we report oracle scores with the corresponding mean and variance of thresholds (τ). For test data, we... | ['Model Schwenk ( 2018 )', 'DE 76.1', 'FR 74.9', 'RU 73.3', 'ZH 71.6', 'DE 76.9', 'FR 75.8', 'RU 73.8', 'ZH 71.6', 'Mean( [ITALIC] τ)', 'Std( [ITALIC] τ)'] | [['Azpeitia et al. ( 2018 )', '84.27', '80.63', '80.89', '76.45', '85.52', '81.47', '81.30', '77.45', '[EMPTY]', '[EMPTY]'], ['LASER (Artetxe and Schwenk, 2019 )', '[BOLD] 95.43', '[BOLD] 92.40', '[BOLD] 92.29', '[BOLD] 91.20', '[BOLD] 96.19', '[BOLD] 93.91', '[BOLD] 93.30', '[BOLD] 92.27', '[EMPTY]', '[EMPTY]'], ['mB... | First observe the mean and variance of the threshold parameters. This is even true for unsupervised models, meaning one could tune the threshold on high-resource pairs and use the same model for mining language pairs without seeing any parallel data. Again, we see strong performance from our Simple encoder, which only ... |
Contextual Lensing of Universal Sentence Representations | 2002.08866 | Table 2: Comparison of USVs on 9 downstream tasks. The first seven tasks are evaluated by training a logistic regression classifier directly on top of the sentence vectors. Performance is accuracy. The last two tasks report Spearman correlation of unsupervised textual similarity. Δ indicates the mean improvement over a... | ['Model', 'MR', 'CR', 'SUBJ', 'MPQA', 'SST', 'TREC', 'MRPC', 'STSb', 'SICK', 'Δ'] | [['BERT CLS (Reimers and Gurevych, 2019 )', '78.68', '84.85', '94.21', '88.23', '84.13', '91.4', '71.13', '16.50', '42.63', '-5.28'], ['Glove BOW (Conneau et al., 2017a )', '77.25', '78.30', '91.17', '87.85', '80.18', '83.0', '72.87', '58.02', '53.76', '-1.88'], ['BERT BOW (Reimers and Gurevych, 2019 )', '78.66', '8... | Here we make two observations. First, our results outperform on aggregate against existing non-BERT universal sentence encoders while only learning a single weight matrix on top of the contextualized embeddings. Second, while SBERT outperforms on aggregate, the Δ is small relative to a basic BERT BOW baseline. This dem... |
Ensemble-Based Deep Reinforcement Learning for Chatbots | 1908.10422 | Table 5: Automatic evaluation of chatbots on test data | ['Agent/Metric', 'Dialogue Reward', 'F1 Score', 'Recall@1'] | [['Upper Bound', '7.7800', '1.0000', '1.0000'], ['Lower Bound', '-7.0600', '0.0796', '0.0461'], ['Ensemble', '[BOLD] -2.8882', '[BOLD] 0.4606', '[BOLD] 0.3168'], ['Single Agent', '-6.4800', '0.1399', '0.0832'], ['Seq2Seq', '-5.7000', '0.2081', '0.1316']] | As expected, the Upper Bound agent achieved the best scores and the Lower Bound agent the lowest scores. The difference in performance between the Ensemble agent and the Seq2Seq agent is significant at p=0.0332 for the Fluency metric and at p<0.01 for the other metrics (Engagingness and Consistency)—based on a two-tail... |
Multi-task Recurrent Model for Speech and Speaker Recognition | 1603.09643 | Table 2: SRE baseline results. | ['System', 'EER% Cosine', 'EER% LDA', 'EER% PLDA'] | [['i-vector (200)', '2.89', '1.03', '0.57'], ['r-vector (256)', '1.84', '1.34', '3.18']] | It can be observed that the i-vector system generally outperforms the r-vector system. Particularly, the discriminative methods (LDA and PLDA) offer much more significant improvement for the i-vector system than for the r-vector system. For this reason, we only consider the simple cosine kernel when scoring r-vectors i... |
Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media | 1911.00637 | Table 1: Distribution of age and gender classes in our data splits | ['[BOLD] Data split', '[BOLD] Under 25 [BOLD] Female', '[BOLD] Under 25 [BOLD] Male', '[BOLD] 25 until 34 [BOLD] Female', '[BOLD] 25 until 34 [BOLD] Male', '[BOLD] 35 and up [BOLD] Female', '[BOLD] 35 and up [BOLD] Male', '[BOLD] #tweets'] | [['[BOLD] TRAIN', '215,950', '213,249', '207,184', '248,769', '174,511', '226,132', '1,285,795'], ['[BOLD] DEV', '27,076', '26,551', '25,750', '31,111', '21,942', '28,294', '160,724'], ['[BOLD] TEST', '26,878', '26,422', '25,905', '31,211', '21,991', '28,318', '160,725'], ['[BOLD] ALL', '269,904', '266,222', '258,839',... | We make use of Arap-Tweet zaghouani2018arap, which we will refer to as Arab-Tweet. Arab-tweet is a dataset of tweets covering 11 Arabic regions from 17 different countries. For each region, data from 100 Twitter users were crawled. Users needed to have posted at least 2,000 and were selected based on an initial list of... |
Lexical-Morphological Modeling forLegal Text Analysis | 1609.00799 | Table 6: Competition results for phases 1 (IR) and 3 (IR + TE) respectively. First three ranked. | ['[BOLD] Rank', '[BOLD] ID', '[BOLD] Prec.', '[BOLD] Recall', '[BOLD] F-m'] | [['1', 'UA1', '0.633', '0.490', '0.552'], ['[BOLD] 2', '[BOLD] JAIST1', '[BOLD] 0.566', '[BOLD] 0.460', '[BOLD] 0.508'], ['3', 'ALV2015', '0.342', '0.529', '0.415']] | The method presented in this paper achieved significant results in COLIEE, being ranked 2nd in phase one (IR) and 3rd in phase three (combined IR + TE). It was not well ranked in phase two (TE). |
The Flores Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English | 1902.01382 | Table 4: Weakly supervised experiments: Adding noisy parallel data from filtered Paracrawl improves translation quality in some conditions. “Parallel” refers to the data described in Table 2. | ['[BOLD] Corpora', '[BOLD] BLEU ne–en', '[BOLD] BLEU si–en'] | [['Parallel', '7.6', '7.2'], ['Unfiltered Paracrawl', '0.43', '0.44'], ['Paracrawl Random', '0.14', '0.36'], ['Paracrawl Clean', '5.9', '7.73'], ['Parallel + Paracrawl Clean', '9.60', '10.86']] | Without any filtering or with random filtering, BLEU score is close to 0 BLEU. Adding Paracrawl Clean to the initial parallel data improves performance by +2.0 and +3.7 BLEU points, for Nepali–English and Sinhala–English, respectively. |
The Flores Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English | 1902.01382 | Table 1: Number of unique sentences (uniq) and total number of sentence pairs (tot) per Flores test set grouped by their original languages. | ['[BOLD] orig lang', '[ITALIC] dev uniq', '[ITALIC] dev tot', '[ITALIC] devtest uniq', '[ITALIC] devtest tot', '[ITALIC] test uniq', '[ITALIC] test tot'] | [['[BOLD] Nepali–English', '[BOLD] Nepali–English', '[BOLD] Nepali–English', '[BOLD] Nepali–English', '[BOLD] Nepali–English', '[BOLD] Nepali–English', '[BOLD] Nepali–English'], ['English', '693', '1,181', '800', '1,393', '850', '1,462'], ['Nepali', '825', '1,378', '800', '1,442', '850', '1,462'], ['[EMPTY]', '[BOLD] 1... | For Sinhala–English, the test set is composed of 850 sentences originally in English, and 850 originally in Sinhala. We have approximately 1.7 translations per sentence. This yielded 1,465 sentence pairs originally in English, and 1,440 originally in Sinhalese, for a total of 2,905 sentences. Similarly, for Nepali–Engl... |
The Flores Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English | 1902.01382 | Table 3: BLEU scores of NMT using various learning settings on devtest (see §3). We report detokenized SacreBLEU Post (2018) for {Ne,Si}→En and tokenized BLEU for En→{Ne,Si}. | ['[EMPTY]', '[BOLD] Supervised', '[BOLD] Supervised +mult.', '[BOLD] Unsupervised', '[BOLD] Unsupervised + mult.', '[BOLD] Semi-supervised it. 1', '[BOLD] Semi-supervised it. 2', '[BOLD] Semi-supervised it 1. + mult.', '[BOLD] Semi-supervised it 2. + mult.', '[BOLD] Weakly supervised'] | [['[BOLD] English–Nepali', '4.27', '6.86', '0.11', '8.34', '6.83', '6.84', '8.76', '8.77', '5.82'], ['[BOLD] Nepali–English', '7.57', '14.18', '0.47', '18.78', '12.725', '15.061', '19.78', '21.45', '9.6'], ['[BOLD] English–Sinhala', '1.23', '-', '0.08', '-', '5.22', '6.46', '-', '-', '3.10'], ['[BOLD] Sinhala–English',... | In the supervised setting, PBSMT performed quite worse than NMT, achieving BLEU scores of 2.5, 4.4, 1.6 and 5.0 on English–Nepali, Nepali–English, English–Sinhala and Sinhala–English, respectively. There are several observations we can make. |
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection | 1911.11951 | Table 2: Performance of various methods on the FNC-I benchmark. The first and second groups are methods introduced during and after the challenge period, respectively. Best results are in bold. | ['Method', '[ITALIC] Accw', 'Acc'] | [['Riedel [ITALIC] et al. ucl', '81.72', '88.46'], ['Hanselowski [ITALIC] et al. athenes', '81.97', '89.48'], ['Baird [ITALIC] et al. talos', '82.02', '89.08'], ['Bhatt [ITALIC] et al. Bhatt:2018:CNS:3184558.3191577', '83.08', '89.29'], ['Borges [ITALIC] et al. Borges2019', '83.38', '89.21'], ['Zhang [ITALIC] et ... | Results of our proposed method, the top three methods in the original Fake News Challenge, and the best-performing methods since the challenge’s conclusion on the FNC- A confusion matrix for our method is presented in the Appendix. To the best of our knowledge, our method achieves state-of-the-art results in weighted-a... |
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection | 1911.11951 | Table 3: Effect of claim-article pair sequence length of FNC-I test set on classification accuracy of RoBERTa model, with a maximum sequence length of 512. | ['Number of Tokens in Example', 'Acc', 'Number of Examples'] | [['<129', '92.05', '2904'], ['129-256', '93.90', '3606'], ['257-384', '95.07', '6328'], ['385-512', '[BOLD] 95.11', '4763'], ['>512', '92.23', '7812'], ['All', '93.71', '25413']] | The model has a maximum sequence length of 512 tokens, so any examples longer than this are trimmed. We find that the model performs best for examples that utilize the full capacity of the input sequence (385 to 512 tokens). Very short sequences (<129 tokens) provide the least amount of information to the model, and th... |
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection | 1911.11951 | Table 4: Effect of maximum sequence length of RoBERTa model on weighted accuracy and classification accuracy. | ['Maximum Number of Tokens', '[ITALIC] Accw', 'Acc'] | [['128', '89.52', '93.46'], ['256', '89.54', '93.48'], ['512', '[BOLD] 90.01', '[BOLD] 93.71']] | We find an increase in accuracy with a longer maximum sequence length, as more context is provided to the model. We cannot increase the length of the input sequence beyond 512 tokens without training the RoBERTa model from scratch, which is not feasible for us. |
An Annotated Corpus of Reference Resolution for Interpreting Common Grounding | 1911.07588 | Table 6: Results of the reference resolution task grouped by the number of referents in the gold annotation (along with the average count of such markables in the test set). | ['# Referents', '% Accuracy', '% Exact Match', 'Count'] | [['0', '95.91±1.38', '83.53±4.65', '0148.5'], ['1', '89.34±0.17', '36.86±1.32', '2782.5'], ['2', '78.14±1.07', '20.59±1.90', '0587.9'], ['3', '70.64±1.02', '13.63±2.06', '0283.3'], ['4', '69.12±2.69', '10.16±3.47', '0081.0'], ['5', '73.57±2.94', '17.56±5.88', '0033.0'], ['6', '78.69±4.45', '13.18±7.31', '0043.0'], ['7'... | To demonstrate the advantages of our approach for interpreting and analyzing dialogue systems, we give a more detailed analysis of TSEL-REF-DIAL model which performed well on all three tasks. In terms of the exact match rate, we found that the model performs very well on 0 and 7 referents: this is because most of them ... |
Focused Meeting Summarization via Unsupervised Relation Extraction | 1606.07849 | Table 4: ROUGE-1 (R-1), ROUGE-2 (R-2) and ROUGE-SU4 (R-SU4) scores for summaries produced by the baselines, GRE [Hachey2009]’s best results, the supervised methods, our method and an upperbound — all with perfect/true DRDA clusterings. | ['[EMPTY]', '[BOLD] True Clusterings [BOLD] R-1', '[BOLD] True Clusterings [BOLD] R-1', '[BOLD] True Clusterings [BOLD] R-1', '[BOLD] True Clusterings [BOLD] R-2', '[BOLD] True Clusterings [BOLD] R-SU4'] | [['[EMPTY]', 'PREC', 'REC', 'F1', 'F1', 'F1'], ['[BOLD] Baselines', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['Longest DA', '34.06', '31.28', '32.61', '12.03', '13.58'], ['Prototype DA', '40.72', '28.21', '33.32', '12.18', '13.46'], ['[BOLD] GRE', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], [... | Note that for GRE based approach, we only list out their best results for utterance-level summarization. If using the salient relation instances identified by GRE as the summaries, the ROUGE results will be significantly lower. When measured by ROUGE-2, our method still have better or comparable performances than other... |
To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation | 1608.01084 | Table 1: The results of our reordering approach using sparse dependency swap (DS) features, in BLEU scores (%) compared to the baseline (Base), on which features are added (∗: significant at p<0.05; ∗∗: significant at p<0.01). We also show the results of prior reordering methods, i.e., dependency distortion penalty (+D... | ['[BOLD] Dataset', '[BOLD] Base', '[BOLD] [BOLD] +DDP', '[BOLD] [BOLD] +Path', '[BOLD] +DDP+Path', '[BOLD] [BOLD] +SHR', '[BOLD] Ours [BOLD] +DDP+DS', '[BOLD] Ours [BOLD] +DDP+Path+DS'] | [['Devset', '40.04', '39.55', '40.51', '40.32', '40.92', '41.39', '41.61'], ['NIST02', '39.19', '39.06', '39.39', '39.81∗∗††', '40.08∗∗', '40.48∗∗††', '40.55∗∗††'], ['NIST03', '39.44', '40.09∗∗', '40.17∗∗', '39.95∗∗', '39.81', '40.88∗∗††', '40.73∗∗†'], ['NIST04', '40.26', '40.16', '40.62∗∗', '40.63∗∗††', '40.39', '40.9... | The distortion limit of all the systems is set to 14, which yields the best result on the development set for the baseline system. As shown in the table, the system with our DS features and DDP on top of the baseline is able to improve over the baseline system without and with DDP, by +1.09 and +0.93 BLEU points respec... |
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation | 1911.08936 | Table 3: Results on DBP15K w.r.t. k values | ['Methods', 'DBPZH-EN H@1', 'DBPZH-EN H@10', 'DBPZH-EN MRR', 'DBPJA-EN H@1', 'DBPJA-EN H@10', 'DBPJA-EN MRR', 'DBPFR-EN H@1', 'DBPFR-EN H@10', 'DBPFR-EN MRR'] | [['GCN', '0.487', '0.790', '0.559', '0.507', '0.805', '0.618', '0.508', '0.808', '0.628'], ['AliNet', '[BOLD] 0.539', '[BOLD] 0.826', '[BOLD] 0.628', '[BOLD] 0.549', '[BOLD] 0.831', '[BOLD] 0.645', '[BOLD] 0.552', '[BOLD] 0.852', '[BOLD] 0.657'], ['AliNet ( [ITALIC] k=3)', '0.461', '0.786', '0.571', '0.484', '0.802', '... | AliNet with 2 layers achieves the best performance over all the three metrics. We observe that when AliNet has more layers, its performance declines as well. Although more layers allow AliNet to indirectly capture more distant neighborhood information by layer-to-layer propagation, such distant neighbors would introduc... |
Learning Word Embeddings with Domain Awareness | 1906.03249 | Table 2: NER results on GENIA. | ['Embeddings', 'P', 'R', 'F1'] | [['CBOW S', '[BOLD] 78.69', '70.75', '74.51'], ['SG S', '76.79', '72.99', '74.84'], ['CBOW T', '76.14', '71.18', '73.57'], ['SG T', '76.89', '71.08', '73.87'], ['CBOW S + T', '76.24', '72.61', '74.38'], ['SG S + T', '75.64', '72.91', '74.25'], ['CBOW S ⊕ T', '75.00', '72.97', '73.97'], ['SG S ⊕ T', '75.47', '72.97', '7... | Overall the embeddings trained in the target domain show worse performance than those trained in the source domain; all the aggregation baselines perform worse than trained on source domain. Such findings indicate that with limited data, the quality of embeddings trained on the target domain is not guaranteed. Interest... |
Learning Word Embeddings with Domain Awareness | 1906.03249 | Table 1: Text classification results on ATIS and IMDB. | ['Embeddings', 'ATIS', 'IMDB'] | [['CBOW S', '96.19', '90.22'], ['SG S', '96.30', '90.16'], ['CBOW T', '95.74', '91.10'], ['SG T', '95.30', '90.67'], ['CBOW S + T', '96.19', '90.32'], ['SG S + T', '96.30', '90.28'], ['CBOW S ⊕ T', '96.09', '90.55'], ['SG S ⊕ T', '95.86', '90.87'], ['CBOW Avg(S,T)', '95.86', '90.38'], ['SG Avg(S,T)', '95.52', '90.45'],... | Overall, in-domain word embeddings tend to outperform out-of-domain ones when in-domain data is relatively large and in a contrary when in-domain data is limited. The aggression methods, i.e., S⊕T, Avg(S,T), normally perform in between the embeddings from the source and the target domain; while the embeddings trained o... |
Learning Word Embeddings with Domain Awareness | 1906.03249 | Table 3: POS tagging results on Twitter. | ['Embeddings', 'P', 'R', 'F1'] | [['CBOW S', '85.13', '85.29', '85.21'], ['SG S', '84.96', '84.86', '84.91'], ['CBOW T', '79.51', '79.78', '79.64'], ['SG T', '79.55', '80.18', '79.86'], ['CBOW S + T', '85.14', '85.29', '85.21'], ['SG S + T', '85.00', '84.89', '84.94'], ['CBOW S ⊕ T', '84.24', '84.62', '84.43'], ['SG S ⊕ T', '84.02', '84.40', '84.21'],... | Owing to the limited target domain data, the overall trend of the results from the source, target and aggregated embeddings is similar to the NER task, while our SG-DI and CBOW-DA also show improvement over all baselines. Such results illustrate the effectiveness of our models in utilizing and combining the source and ... |
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications | 1711.05073 | Table 8: Performance on various question types. Current MRC models achieve impressive improvements compared with the selected paragraph baseline. However, there is a large gap between these models and human. | ['[BOLD] Question type', '[BOLD] Description BLEU-4%', '[BOLD] Description Rouge-L%', '[BOLD] Entity BLEU-4%', '[BOLD] Entity Rouge-L%', '[BOLD] YesNo BLEU-4%', '[BOLD] YesNo Rouge-L%'] | [['[BOLD] Match-LSTM', '32.8', '40.0', '29.5', '38.5', '5.9', '7.2'], ['[BOLD] BiDAF', '32.6', '39.7', '29.8', '38.4', '5.5', '7.5'], ['[BOLD] Human', '58.1', '58.0', '44.6', '52.0', '56.2', '57.4']] | We can see that both the models and human achieve relatively good performance on description questions, while YesNo questions seem to be the hardest to model. We consider that description questions are usually answered with long text on the same topic. This is preferred by BLEU or Rouge. However, the answers to YesNo q... |
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension | 1804.07927 | Table 1: Comparison between various RC datasets | ['[BOLD] Metrics for Comparative Analysis', '[BOLD] Movie QA', '[BOLD] NarrativeQA over plot-summaries', '[BOLD] Self-RC', '[BOLD] Paraph-raseRC'] | [['Avg. word distance', '20.67', '24.94', '13.4', '45.3'], ['Avg. sentence distance', '1.67', '1.95', '1.34', '2.7'], ['Number of sentences for inferencing', '2.3', '1.95', '1.51', '2.47'], ['% of instances where both Query & Answer entities were found in passage', '67.96', '59.4', '58.79', '12.25'], ['% of instances w... | Fig. We use NER and noun phrase/verb phrase extraction over the entire dataset to identify key entities in the question, plot and answer which are in turn used to compute the metrics mentioned in the table. The metrics “Avg word distance” and “Avg sentence distance” indicate the average distance (in terms of words/sent... |
Context is Key: Grammatical Error Detection with Contextual Word Representations | 1906.06593 | Table 3: Error detection F0.5 of different embedding integration strategies (‘input’ vs. ‘output’) per model on all datasets. | ['Flair', 'Input', 'Shared Task Dev 36.45', 'CoNLL Test 1 25.79', 'CoNLL Test 2 34.35', 'FCE test 49.97', 'JFLEG test 54.08'] | [['[EMPTY]', 'Output', '33.47', '24.52', '33.18', '48.50', '52.10'], ['ELMo', 'Input', '42.96', '29.14', '40.15', '52.81', '58.54'], ['[EMPTY]', 'Output', '37.33', '27.33', '38.10', '52.99', '54.86'], ['BERT base', 'Input', '[BOLD] 48.50', '35.70', '46.29', '[BOLD] 57.28', '[BOLD] 61.98'], ['[EMPTY]', 'Output', '46.33'... | We observe that, although performance varies across datasets and models, integration by concatenation to the word embeddings yields the best results across the majority of datasets for all models (BERT: 3/5 datasets; ELMo: 4/5 datasets; Flair: 5/5 datasets). The lower integration point allows the model to learn more le... |
Massively Multilingual Transfer for NER | 1902.00193 | Table 2: The performance of RaRe and BEA in terms of phrase-based F1 on CoNLL NER datasets compared with state-of-the-art benchmark methods. Resource requirements are indicated with superscripts, p: parallel corpus, w: Wikipedia, d: dictionary, l: 100 NER annotation, 0: no extra resources. | ['lang.', 'de', 'es', 'nl', 'en'] | [['Täckström et\xa0al. ( 2012 ) [ITALIC] p', '40.40', '59.30', '58.40', '—'], ['Nothman et\xa0al. ( 2013 ) [ITALIC] w', '55.80', '61.00', '64.00', '61.30'], ['Tsai et\xa0al. ( 2016 ) [ITALIC] w', '48.12', '60.55', '61.60', '—'], ['Ni et\xa0al. ( 2017 ) [ITALIC] w, [ITALIC] p, [ITALIC] d', '58.50', '65.10', '65.40', '—'... | S4SS0SSS0Px4 CoNLL Dataset Finally, we apply our model to the CoNLL-02/03 datasets, to benchmark our technique against related work. This corpus is much less rich than Wikiann used above, as it includes only four languages (en, de, nl, es), and furthermore, the languages are closely related and share the same script. N... |
Massively Multilingual Transfer for NER | 1902.00193 | Table 3: The effect of the choice of monolingual word embeddings (Common Crawl and Wikipedia), and their cross-lingual mapping on NER direct transfer. Word translation accuracy, and direct transfer NER F1 are averaged over 40 languages. | ['Unsup', 'crawl', 'Transl. Acc. 34', 'Dir.Transf. F1 26'] | [['Unsup', 'wiki', '24', '21'], ['IdentChar', 'crawl', '43', '37'], ['IdentChar', 'wiki', '[BOLD] 53', '[BOLD] 44'], ['Sup', 'crawl', '50', '39'], ['Sup', 'wiki', '[BOLD] 54', '[BOLD] 45']] | We experimented with Wiki and CommonCrawl monolingual embeddings from fastText Bojanowski et al. Each of the 41 languages is mapped to English embedding space using three methods from MUSE: 1) supervised with bilingual dictionaries; 2) seeding using identical character sequences; and 3) unsupervised training using adve... |
Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects? | 1703.09400 | TABLE II: Performance of the methods on the 11Headlines2Media Corpus[] dataset | ['Method Without Pre-trained Vectors', 'Method *Chakroborty et al.\xa0', 'Precision 0.95', 'Recall 0.90', 'F-measure 0.93', 'Accuracy 0.93', 'Cohen’s [ITALIC] κ', 'ROC-AUC 0.97'] | [['Without Pre-trained Vectors', 'Skip-Gram [ITALIC] sw', '0.976', '0.975', '0.975', '0.976', '0.952', '0.976'], ['With Pre-trained Vectors', '*Anand et al.\xa0', '0.984', '0.978', '0.982', '0.982', '[EMPTY]', '0.998'], ['With Pre-trained Vectors', 'Skip-Gram [ITALIC] sw+ Google_word2vec', '0.977', '0.977', '0.977', '0... | We use the \IfEqCase11Headlines2Media Corpus[] dataset to evaluate our classification model. We perform 10-fold cross-validation to evaluate various methods with respect to accuracy, precision, recall, f-measure, area under the ROC curve (ROC-AUC) and Cohen’s κ. To avoid randomness effect, we perform each experiment 5 ... |
Effective Sentence Scoring Method using Bidirectional Language Modelfor Speech Recognition | 1905.06655 | Table 2: WERs for unidirectional and bidirectional SANLMs interpolated with the baseline model on LibriSpeech | ['Model', '| [ITALIC] V|', 'dev clean', 'dev other', 'test clean', 'test other'] | [['baseline', '[EMPTY]', '7.17', '19.79', '7.26', '20.37'], ['+ uniSANLM', '10k', '6.09', '17.50', '6.08', '18.33'], ['+ uniSANLM', '20k', '6.05', '17.48', '6.11', '18.25'], ['+ uniSANLM', '40k', '6.08', '17.32', '6.11', '18.13'], ['+ biSANLM', '10k', '5.65', '16.85', '5.69', '17.59'], ['+ biSANLM', '20k', '5.57', '16.... | The WER results show that the biSANLM with our approach is consistently and significantly better than the uniSANLM regardless of the test set and the vocabulary size. |
When and Why is Unsupervised Neural Machine Translation Useless? | 2004.10581 | Table 3: Unsupervised NMT performance where source and target training data are from different domains. The data size on both sides is the same (20M sentences). | ['Domain ( [BOLD] en)', 'Domain ( [BOLD] de/ [BOLD] ru)', 'Bleu [%] [BOLD] de-en', 'Bleu [%] [BOLD] en-de', 'Bleu [%] [BOLD] ru-en', 'Bleu [%] [BOLD] en-ru'] | [['Newswire', 'Newswire', '23.3', '19.9', '11.9', '9.3'], ['Newswire', 'Politics', '11.5', '12.2', '2.3', '2.5'], ['Newswire', 'Random', '18.4', '16.4', '6.9', '6.1']] | (News Crawl). The results show that the domain matching is critical for unsupervised NMT. For instance, although German and English are very similar languages, we see the performance of German↔English deteriorate down to -11.8 % Bleu by the domain mismatch. |
EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding | 1507.08240 | Table 2: Comparisons of decoding speed between the phoneme-based Eesen system and the hybrid HMM/DNN system. “RTF” refers to the real-time factor in decoding. “Graph Size” means the size of the decoding graph in terms of megabytes. | ['Model', 'RTF', 'Graph Size'] | [['Eesen RNN', '0.64', '263'], ['Hybrid HMM/DNN', '2.06', '480']] | A major advantage of Eesen compared with the hybrid approach is the decoding speed. The acceleration comes from the drastic reduction of the number of states, i.e., from thousands of senones to tens of phonemes/characters. From their real-time factors, we observe that decoding in Eesen is 3.2× faster than that of HMM/D... |
KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation | 2004.04100 | Table 6: Manual evaluation. The best results (t-test, p-value < 0.005) are in bold. Between two generative models, the significantly better results are italic underlined (t-test, p-value < 0.005) or underlined (t-test, p-value < 0.05). κ is the Fleiss’ kappa value. “+ know” means the models enhanced by knowledge inform... | ['[BOLD] Model', '[BOLD] Fluency', '[BOLD] Coherence'] | [['[BOLD] Film\xa0∖\xa0 [ITALIC] κ', '0.50', '0.61'], ['[BOLD] HRED', '1.64', '1.19'], ['[BOLD] HRED + know', '[ITALIC] 1.78', '[ITALIC] 1.28'], ['[BOLD] BERT + know', '[BOLD] 2.00', '[BOLD] 1.79'], ['[BOLD] Music\xa0∖\xa0 [ITALIC] κ', '0.37', '0.57'], ['[BOLD] HRED', '1.90', '1.30'], ['[BOLD] HRED + know', '1.86', '1.... | 4.4.3 Results As can be seen, knowledge-aware BERT outperforms other models significantly in both metrics in all the three domains, which agrees with the results of automatic evaluation. The Fluency is 2.00 because the retrieved responses are all human-written sentences. The Fluency scores of both generation-based mode... |
How Can We Know What Language Models Know? | 1911.12543 | Table 14: Micro-averaged accuracy (%) before and after LM-aware prompt fine-tuning. | ['[BOLD] Prompts', '[BOLD] Top1', '[BOLD] Top3', '[BOLD] Top5', '[BOLD] Opti.', '[BOLD] Oracle'] | [['before', '31.9', '34.5', '33.8', '38.1', '47.9'], ['after', '30.2', '32.5', '34.7', '37.5', '50.8']] | After fine-tuning, the oracle performance increased significantly, while the ensemble performances (both rank-based and optimization-based) dropped slightly. This indicates that LM-aware fine-tuning has the potential to discover better prompts, but some portion of the refined prompts may have over-fit to the training s... |
How Can We Know What Language Models Know? | 1911.12543 | Table 7: Ablation study of middle-word and dependency-based prompts on BERT-base. | ['[BOLD] Prompts', '[BOLD] Top1', '[BOLD] Top3', '[BOLD] Top5', '[BOLD] Opti.', '[BOLD] Oracle'] | [['[BOLD] Mid', '30.7', '32.7', '31.2', '36.9', '45.1'], ['[BOLD] Mid+Dep', '31.4', '34.2', '34.7', '38.9', '50.7']] | Middle-word vs. Dependency-based The improvements confirm our intuition that words belonging to the dependency path but not in the middle of the subject and object are also indicative of the relation. |
How Can We Know What Language Models Know? | 1911.12543 | Table 8: Micro-averaged accuracy (%) of various LMs | ['[BOLD] Model', '[BOLD] Man', '[BOLD] Mine', '[BOLD] Mine [BOLD] +Man', '[BOLD] Mine [BOLD] +Para', '[BOLD] Man [BOLD] +Para'] | [['BERT', '31.1', '38.9', '39.6', '36.2', '37.3'], ['ERNIE', '32.1', '42.3', '43.8', '40.1', '41.1'], ['KnowBert', '26.2', '34.1', '34.6', '31.9', '32.1']] | , we compare BERT with ERNIE and KnowBert, which are enhanced with external knowledge by explicitly incorporating entity embeddings. ERNIE outperforms BERT by 1 point even with the manually defined prompts, but our prompt generation methods further emphasize the difference between the two methods, with the highest accu... |
How Can We Know What Language Models Know? | 1911.12543 | Table 10: Micro-averaged accuracy (%) on Google-RE. | ['[BOLD] Model', '[BOLD] Man', '[BOLD] Mine', '[BOLD] Mine [BOLD] +Man', '[BOLD] Mine [BOLD] +Para', '[BOLD] Man [BOLD] +Para'] | [['BERT-base', '9.8', '10.0', '10.4', '9.6', '10.0'], ['BERT-large', '10.5', '10.6', '11.3', '10.4', '10.7']] | Again, ensembling diverse prompts improves accuracies for both the BERT-base and BERT-large models. The gains are somewhat smaller than those on the T-REx subset, which might be caused by the fact that there are only 3 relations and one of them (predicting the birth-date of a person) is particularly hard to the extent ... |
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation | 1806.04441 | Table 3: Ablation experiment on navigation domain. -copy refers to a framework without copying. -RL refers to a framework without RL loss. | ['[BOLD] Model', '[BOLD] BLEU', '[BOLD] Macro F1', '[BOLD] Micro F1'] | [['our model', '[BOLD] 13.7', '[BOLD] 62.0', '[BOLD] 56.9'], ['-copying', '9.6', '35.2', '41.3'], ['-RL', '9.3', '38.2', '46.0']] | In this section, we perform several ablation experiments to evaluate different components in our framework on the navigation domain. The results demonstrate effectiveness of components of our model to the final performance. |
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation | 1806.04441 | Table 2: Automatic evaluation on test data. Best results are shown in bold. Generally, our framework outperforms other models in most automatic evaluation metrics. | ['[BOLD] Model', '[BOLD] Navigation [BOLD] BLEU', '[BOLD] Navigation [BOLD] Macro F1', '[BOLD] Navigation [BOLD] Micro F1', '[BOLD] Weather [BOLD] BLEU', '[BOLD] Weather [BOLD] Macro F1', '[BOLD] Weather [BOLD] Micro F1'] | [['Seq2Seq with Attention', '8.3', '15.6', '17.5', '[BOLD] 19.6', '56.0', '53.5'], ['Copy Net', '8.7', '20.8', '23.7', '17.5', '52.4', '53.1'], ['KV Net', '8.7', '24.9', '29.5', '12.4', '37.7', '39.4'], ['our model', '[BOLD] 13.7', '[BOLD] 62.0', '[BOLD] 56.9', '14.9', '[BOLD] 58.5', '[BOLD] 56.3']] | The results show that our model outperforms other models in most of automatic evaluation metrics. In the navigation domain, compared to KV Net, we achieve 5.0 improvement on BLEU score, 37.1 improvement on Macro F1 and 27.4 improvement on Micro F1. Compared to Copy Net, we achieve 5.0 improvement on BLEU score, 41.2 im... |
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation | 1806.04441 | Table 4: Human evaluation of responses based on random selected previous dialogue history in test dataset. The agreement scores indicate the percentage of responses to which all three human experts give exactly the same scores. | ['[BOLD] Model', '[BOLD] Correct', '[BOLD] Fluent', '[BOLD] Humanlike'] | [['Copy Net', '3.52', '4.47', '4.17'], ['KV Net', '3.61', '4.50', '4.20'], ['our model', '[BOLD] 4.21', '[BOLD] 4.65', '[BOLD] 4.38'], ['agreement', '41.0', '55.0', '43.0']] | In this section, we provide human evaluation on our framework and other baseline models. We randomly generated 200 responses. These response are based on distinct dialogue history in navigation test data. We hire three different human experts to evaluate the quality of responses. Three dimensions are involved, which ar... |
Linguistic Geometries for Unsupervised Dimensionality Reduction | 1003.0628 | Table 2: Three evaluation measures (i), (ii), and (iii) (see the beginning of the section for description) for convex combinations (8) using different values of α. The first four rows represent methods A, B, C, and D. The bottom row represents a convex combination whose coefficients were obtained by searching for the m... | ['( [ITALIC] α1, [ITALIC] α2, [ITALIC] α3, [ITALIC] α4)', '(i)', '(ii)', '(iii) (k=5)'] | [['(1,0,0,0)', '0.5756', '-3.9334', '0.7666'], ['(0,1,0,0)', '0.5645', '-4.6966', '0.7765'], ['(0,0,1,0)', '0.5155', '-5.0154', '0.8146'], ['(0,0,0,1)', '0.6035', '-3.1154', '0.8245'], ['(0.3,0.4,0.1,0.2)', '[BOLD] 0.4735', '[BOLD] -5.1154', '[BOLD] 0.8976']] | We also examined convex combinations α1HA+α2HB+α3HC+α4HD (8) with ∑αi=1 and αi≥0. The beginning of the section provides more information on these measures. The first four rows correspond to the “pure” methods A,B,C,D. The bottom row correspond to a convex combination found by minimizing the unsupervised evaluation meas... |
SCALABLE MULTILINGUAL FRONTEND FOR TTS | 2004.04934 | Table 4: Testing Accuracy – single model, unspliced and spliced | ['Locale', 'Combined BLEU', 'Combined chrF3', 'Combined, Spliced BLEU', 'Combined, Spliced chrF3'] | [['de-DE', '92.01', '0.9484', '94.82', '0.9782'], ['en-US', '92.94', '0.9428', '96.84', '0.9822'], ['es-ES', '91.51', '0.9246', '99.54', '0.9969'], ['nl-NL', '94.42', '0.9509', '97.41', '0.9826'], ['ru-RU', '94.46', '0.9558', '98.48', '0.9919'], ['sv-SE', '97.39', '0.9789', '98.41', '0.9891']] | Generally the accuracy was lower, but still very reasonable for most synthesis cases. This table also illustrates the significant performance boost achieved by using the splicing technique. |
SCALABLE MULTILINGUAL FRONTEND FOR TTS | 2004.04934 | Table 3: Testing Accuracy – dual model | ['Locale', 'Normalization BLEU', 'Normalization chrF3', 'Pronunciation BLEU', 'Pronunciation chrF3'] | [['en-US', '99.69', '0.9991', '97.09', '0.9926'], ['es-ES', '99.79', '0.9990', '99.88', '0.9996'], ['it-IT', '99.80', '0.9994', '99.71', '0.9991'], ['pt-PT', '99.85', '0.9993', '99.68', '0.9992'], ['fr-FR', '99.70', '0.9991', '99.52', '0.9985'], ['sv-SE', '99.10', '0.9934', '99.34', '0.9970'], ['nl-NL', '98.13', '0.985... | Generally the accuracy was lower, but still reasonable for most synthesis cases. For longer sentences the test outputs are created by splicing multiple shorter outputs. |
Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling | 2005.06634 | Table 5: Average Prediction for BERTLARGE, BioBert, ClinicalBert and Max Ensemble Results with and without Preprocessor | ['[ITALIC] No preprocessor', '[BOLD] BERT', '[BOLD] BioBERT', '[BOLD] ClinicalBERT', '[BOLD] Max Ensemble'] | [['[ITALIC] F1-score', '0.6446', '0.5915', '0.5809', '0.6378'], ['Precision', '0.6695', '0.6200', '0.6180', '0.5663'], ['Recall', '0.6214', '0.5655', '0.5479', '0.7300'], ['[ITALIC] Preprocessor', '[BOLD] BERT PP', '[BOLD] BioBERT PP', '[BOLD] ClinicalBERT PP', '[BOLD] Max Ensemble PP'], ['[ITALIC] F1-score', '0.6475',... | However, the difference in performance lessened considerably when the preprocessor was applied, with ClinicalBERT in particular observing improved predictions, with F1-score increasing from 0.58 to 0.62 and recall increasing from 0.55 to 0.61. Despite the poorer performance of BioBERT and ClinicalBERT, ensemble methods... |
Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling | 2005.06634 | Table 3: Average Prediction on Test set for Baseline BERT | ['[EMPTY]', '[BOLD] 1', '[BOLD] 2', '[BOLD] 3', '[BOLD] 4', '[BOLD] 5', '[BOLD] Avg', '[BOLD] Chen1'] | [['[ITALIC] F1', '0.59', '0.63', '0.62', '0.63', '0.62', '[BOLD] 0.618', '0.618'], ['P', '0.64', '0.66', '0.64', '0.66', '0.67', '[BOLD] 0.654', '0.646'], ['R', '0.55', '0.61', '0.61', '0.60', '0.57', '[BOLD] 0.587', '0.593']] | Our baseline model, using the SMM4H winning team’s model parameters but without their corpus for retraining, reported as ”BERT_noRetrained” Chen et al. We therefore ran the model multiple times and only included the results of the first five models with non-zero scores as our baseline. Notably, the F1-score is nearly i... |
Syntactic Scaffolds for Semantic Structures | 1808.10485 | Table 1: Frame SRL results on the test set of FrameNet 1.5., using gold frames. Ensembles are denoted by †. | ['[BOLD] Model', '[BOLD] Prec.', '[BOLD] Rec.', '[ITALIC] F1'] | [['Kshirsagar et\xa0al. ( 2015 )', '66.0', '60.4', '63.1'], ['Yang and Mitchell ( 2017 ) (Rel)', '71.8', '57.7', '64.0'], ['Yang and Mitchell ( 2017 ) (Seq)', '63.4', '66.4', '64.9'], ['†Yang and Mitchell ( 2017 ) (All)', '70.2', '60.2', '65.5'], ['Semi-CRF baseline', '67.8', '66.2', '67.0'], ['+ constituent identity',... | We follow the official evaluation from the SemEval shared task for frame-semantic parsing Baker et al. Our semi-CRF baseline outperforms all prior work, without any syntax. This highlights the benefits of modeling spans and of global normalization. Contemporaneously with this work, Peng et al. We evaluated their output... |
Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest | 1506.08126 | Table 1: Comparison between the methods. Score s4 corresponds to pairs for which the seven judges agreed more significantly (a difference of 4+ votes). Score s3 requires a difference of 3+ votes. Score s includes all pairs (about 850 per method, minus a small number of errors). The best methods (CU2R, CU3, OR2, and CU2... | ['[BOLD] Category', '[BOLD] Code', '[BOLD] Method', '[ITALIC] n4', '[ITALIC] s4', '[ITALIC] n3', '[ITALIC] s3', '[ITALIC] n', '[ITALIC] s'] | [['Centrality', 'OR1R', 'least similar to centroid', '308', '-2.73', '453', '-2.14', '846', '-1.26'], ['[EMPTY]', '[BOLD] OR2', 'highest lexrank', '302', '[BOLD] 1.39', '457', '[BOLD] 1.11', '846', '[BOLD] 0.59'], ['[EMPTY]', 'OR2R', 'smallest lexrank', '317', '-0.61', '450', '-0.58', '846', '-0.29'], ['[EMPTY]', 'OR3R... | Each evaluation (ni, si pair) corresponds to the number of votes in favor of the given method minus the number of votes against. So the first set corresponds to pairs in which, out of seven judges, there was a difference of at least 4 votes in favor of one or the other caption. This level of significant agreement happe... |
Variational Question-Answer Pair Generation for Machine Reading Comprehension | 2004.03238 | Table 3: Results for answer extraction on the test set. For all the metrics, higher is better. | ['[EMPTY]', 'Relevance Precision', 'Relevance Precision', 'Relevance Recall', 'Relevance Recall', 'Diversity Dist'] | [['[EMPTY]', 'Prop.', 'Exact', 'Prop.', 'Exact', 'Dist'], ['NER', '34.44', '19.61', '64.60', '45.39', '30.0k'], ['BiLSTM-CRF w/ char w/ NER (Du18)', '45.96', '33.90', '41.05', '28.37', '-'], ['VQAG', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]'], ['{\\rm C=0}', '[BOLD] 58.39', '[BOLD] 47.15', '21.82', '16.38',... | Our model with the condition {\rm C=5} performed the best in terms of the recall scores, while surpassing NER in terms of diversity. From the viewpoint of diversity, {\rm C=20} is the best setting. However, high Dist scores do not occur together with high recall scores. This observation shows the trade-off between dive... |
Variational Question-Answer Pair Generation for Machine Reading Comprehension | 2004.03238 | Table 2: QA pair modeling capacity measured on the test set. NLL: negative log likelihood (-\log p(q,a|c)). {\rm NLL}_{a}=-\log p(a|c), {\rm NLL}_{q}=-\log p(q|a,c). D_{{\rm KL}_{z}} and D_{{\rm KL}_{y}} are Kullback–Leibler divergence between the approximate posterior and the prior of the latent variable z and y. The ... | ['[EMPTY]', 'NLL', '{\\rm NLL}_{a}', '{\\rm NLL}_{q}', 'D_{{\\rm KL}_{z}}', 'D_{{\\rm KL}_{y}}'] | [['Pipeline', '36.26', '3.99', '32.50', '-', '-'], ['VQAG', 'VQAG', 'VQAG', 'VQAG', '[EMPTY]', '[EMPTY]'], ['{\\rm C=0}', '[BOLD] 34.46', '4.46', '30.00', '0.027', '0.036'], ['{\\rm C=5}', '37.00', '5.15', '31.51', '4.862', '4.745'], ['{\\rm C=20}', '59.66', '14.38', '43.56', '17.821', '17.038'], ['{\\rm C=100}', '199.... | We also observe that this issue happens when implementing our model according to the Ineq. To mitigate this problem, inspired by Prokhorv19, we use modified \beta-VAE beta-vae proposed by Burgess18, which uses two hyperparameters to control the KL terms. Our modified variational lower bound is as follows: \displaystyle... |
COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval | 1805.08661 | TABLE IV: Automated evaluation of different models for image tagging. Cascading MLP learned from cross-lingual data is the best. | ['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F-measure'] | [['Clarifai\xa0', '0.217', '0.261', '0.228'], ['MLP trained on COCO-MT', '0.432', '0.525', '0.456'], ['MLP trained on COCO-CN', '0.477', '0.576', '0.503'], ['Multi-task MLP', '0.482', '0.583', '0.508'], ['Cascading MLP', '[BOLD] 0.491', '[BOLD] 0.594', '[BOLD] 0.517']] | The proposed Cascading MLP tops the performance. Although the number of training images in COCO-MT is 6.6 times as large as COCO-CN, the MLP trained on COCO-CN outperforms its COCO-MT counterpart with a clear margin. This result shows the importance of high-quality annotation for training. Some image tagging results ar... |
COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval | 1805.08661 | TABLE V: Human evaluation of different models for image tagging on COCO-CN test100. Cascading MLP again performs the best. | ['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F-measure'] | [['Clarifai', '0.634', '0.358', '0.451'], ['MLP trained on COCO-MT', '0.778', '0.453', '0.563'], ['MLP trained on COCO-CN', '0.836', '0.488', '0.607'], ['Cascading MLP', '[BOLD] 0.858', '[BOLD] 0.501', '[BOLD] 0.623']] | Human evaluation. It is possible that the lower performance of Clarifai is caused by a discrepancy between the Chinese vocabulary of the online service and our ground-truth vocabulary. To resolve this uncertainty, we performed a user study as follows. For each of the 100 pre-specified images, we collected the top 5 pre... |
COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval | 1805.08661 | TABLE VIII: Automated evaluation of image captioning models trained on different datasets. The proposed cross-lingual transfer performs the best. | ['[BOLD] Training', '[BOLD] BLEU-4', '[BOLD] METEOR', '[BOLD] ROUGE-L', '[BOLD] CIDEr'] | [['Flickr8k-CN', '10.1', '14.9', '33.8', '22.9'], ['AIC-ICC', '7.4', '21.3', '34.2', '24.6'], ['COCO-MT', '30.2', '27.1', '50.0', '86.2'], ['COCO-CN', '31.7', '27.2', '52.0', '84.6'], ['COCO-Mixed', '29.8', '28.6', '50.3', '86.8'], ['Transfer learning ', '33.7', '28.2', '52.9', '89.2'], ['Artificial token ', '31.8', '2... | Models trained on Flickr8k-CN and AIC-ICC have fairly low scores. The relatively limited size of Flickr8k-CN makes it lack the ability for training a good captioning model. As for AIC-ICC, although it is the largest Chinese captioning dataset, it is strongly biased that all the images are about human beings. Consequent... |
Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention | 1808.03227 | TABLE IV: Cross-corpus results (F-score in %). Rows correspond to training corpora and columns to testing. Models marked with † represents tLSTM and ‡ represents tLSTM+tAttn | ['[EMPTY]', '[ITALIC] AIMed', '[ITALIC] BioInfer', '[ITALIC] IEPA', '[ITALIC] HPRD50', '[ITALIC] LLL'] | [['AIMed †', '−', '47.0', '38.6', '41.5', '34.6'], ['AIMed ‡', '−', '45.0', '37.9', '39.1', '33.5'], ['BioInfer †', '50.8', '−', '40.8', '43.7', '35.0'], ['BioInfer ‡', '50.0', '−', '40.0', '45.5', '33.5']] | Rows correspond to the training corpora and columns correspond to the test corpora. It is clearly visible that the performance degrades on all of the corpora as the training and testing sets are not from the same distribution which goes against the fundamental machine learning theory about training and test sets being ... |
Automatic Disambiguation ofFrench Discourse Connectives | 1704.05162 | Table 6: Information Gain of Each Feature in the Disambiguation of Discourse Connectives | ['[EMPTY]', '[BOLD] Feature', '[BOLD] French', '[BOLD] English'] | [['[ITALIC] Lexical:', '[ITALIC] Conn', '0.352', '0.351'], ['[ITALIC] Syntactic:', '[ITALIC] SelfCat', '0.167', '0.468'], ['[EMPTY]', '[ITALIC] SelfCatLeftSibling', '0.108', '0.145'], ['[EMPTY]', '[ITALIC] SelfCatParent', '0.093', '0.292'], ['[EMPTY]', '[ITALIC] Pos', '0.045', '0.119'], ['[EMPTY]', '[ITALIC] SelfCatRig... | To evaluate the contribution of each feature, we ranked the features by their information gain for both languages. For example, the Selfcat feature has a significantly lower information gain than the Conn feature for the disambiguation of French discourse connectives while this is not the case for English discourse con... |
Creative Procedural-Knowledge Extraction From Web Design Tutorials | 1904.08587 | Table 5: BLEU scores for the usage summarization task evaluated in the validation and testing sets. The best performance in either set is bolded. The naming schema of different algorithms: [number of layers]-layer-[whether or not the attention mechanism Bahdanau et al. (2014) is applied]. | ['dropout', 'validation 0', 'validation 0.2', 'validation 0.5', 'testing 0', 'testing 0.2', 'testing 0.5'] | [['1-layer', '11.73', '12.29', '13.49', '10.33', '11.71', '12.17'], ['1-layer-att', '18.45', '19.18', '[BOLD] 21.53', '17.24', '17.84', '[BOLD] 19.70'], ['2-layer', '11.37', '11.97', '13.16', '10.03', '11.30', '12.56'], ['2-layer-att', '16.37', '16.23', '17.18', '14.83', '15.27', '16.85']] | An usage summarization module takes a raw sentence as the input and generates a command usage summary. A natural model that can accomplish this task is the sequence-to-sequence model Sutskever et al. Then a seperate RNN-based decoder takes the representation as the input and sequentially generates a list of words as th... |
Sound-Word2Vec: Learning Word Representations Grounded in Sounds | 1703.01720 | Table 1: Text-based sound retrieval (higher is better). We find that our sound-word2vec model outperforms all baselines. | ['Embedding', 'Recall @1', 'Recall @10', 'Recall @50', 'Recall @100'] | [['word2vec', '6.47±0.00', '14.25±0.05', '21.72±0.12', '26.03±0.22'], ['tag-word2vec', '6.95±0.02', '15.10±0.03', '22.43±0.09', '27.21±0.24'], ['sound-word2vec(r)', '6.49±0.00', '14.98±0.03', '21.96±0.11', '26.43±0.20'], ['(Lopopolo and van Miltenburg, 2015 )', '6.48±0.02', '15.09±0.05', '21.82±0.13', '26.89±0.23'], [... | Given a textual description of a sound as query, we compare it with tags associated with sounds in the database to retrieve the sound with the closest matching tags. Note that this is a purely textual task, albeit one that needs awareness of sound. In a sense, this task exactly captures what we want our model to be abl... |
Sound-Word2Vec: Learning Word Representations Grounded in Sounds | 1703.01720 | Table 2: Comparison to state of the art AMEN and ASLex datasets (Kiela and Clark, 2015) (higher is better). Our approach performs better than Kiela and Clark (2015). | ['Embedding', 'Spearman Correlation [ITALIC] ρs AMEN', 'Spearman Correlation [ITALIC] ρs ASLex'] | [['(Lopopolo and van Miltenburg, 2015 )', '0.410±0.09', '0.237±0.04'], ['(Kiela and Clark, 2015 )', '0.648±0.08', '0.366±0.11'], ['sound-word2vec', '[BOLD] 0.674±0.05', '[BOLD] 0.391±0.06']] | In addition to enlarging the vocabulary, the pre-training helps induce smoothness in the sound-word2vec embeddings – allowing us to transfer semantics learnt from sounds to words that were not present as tags in the Freesound database. Indeed, we find that word2vec pre-training helps improve performance (Sec. |
Improving text classification with vectors of reduced precisionThis research was supported in part by Faculty of Management and Social Communication of the Jagiellonian University and PLGrid Infrastructure. | 1706.06363 | TABLE V: Times of training and testing for the classifiers on the corpora without or with SVD with different number of components. | ['[BOLD] Classifier', '[BOLD] Variant', '[BOLD] Training (seconds) webkb', '[BOLD] Training (seconds) r8', '[BOLD] Training (seconds) r52', '[BOLD] Training (seconds) ng20', '[BOLD] Training (seconds) cade', '[BOLD] Testing (seconds) webkb', '[BOLD] Testing (seconds) r8', '[BOLD] Testing (seconds) r52', '[BOLD] Testing... | [['KNN 1', 'no SVD', '1.44', '0.44', '0.48', '3.22', '24.86', '0.50', '0.50', '0.66', '3.56', '11.78'], ['KNN 1', 'SVD(100)', '10.48', '10.32', '10.23', '17.23', '55.77', '0.54', '0.38', '0.43', '1.54', '9.05'], ['KNN 1', 'SVD(500)', '51.12', '55.34', '55.38', '78.15', '204.10', '0.57', '0.48', '0.60', '2.23', '12.96']... | SVD is the most time–consuming phase in training in comparison to classification. However, it can reduce time of testing. Time of testing using KNNs is higher than other classifiers, because it is proportional to number of documents. Time of precision reduction is negligible. |
Multi-Field Structural Decomposition for Question Answering | 1604.00938 | Table 1: Results from our question-answering system on 8 types of questions in the bAbI tasks. | ['Type', 'Lexical [ITALIC] λ=1', 'Lexical [ITALIC] λ=1', 'Lexical [ITALIC] λ is learned', 'Lexical [ITALIC] λ is learned', 'Lexical + Syntax [ITALIC] λ=1', 'Lexical + Syntax [ITALIC] λ=1', 'Lexical + Syntax [ITALIC] λ is learned', 'Lexical + Syntax [ITALIC] λ is learned', 'Lexical + Syntax + Semantics [ITALIC]... | [['Type', 'MAP', 'MRR', 'MAP', 'MRR', 'MAP', 'MRR', 'MAP', 'MRR', 'MAP', 'MRR', 'MAP', 'MRR'], ['1 (qa1)', '39.62', '61.73', '39.62', '61.73', '29.90', '48.05', '40.50', '61.47', '72.60', '85.07', '[BOLD] 100.0', '[BOLD] 100.0'], ['2 (qa4)', '62.90', '81.45', '62.90', '81.45', '64.00', '82.00', '64.00', '82.00', '55.70... | The map and mrr show clear correlation with respect to the number of active fields. For the majority of tasks, using only the lexical fields does not perform well. The fictional stories included in this data often contain multiple occurrences of the same lexicons, and the lexical fields alone are not able to select the... |
Multi-label Dataless Text Classification with Topic Modeling | 1711.01563 | Table 2: Performance comparison on the two datasets. The best and the second best results by dataless classifiers are highlighted in boldface and underlined respectively. #F1: Macro-F1 score; #AUC: Macro-AUC score. | ['Method', 'Ohsumed # [ITALIC] F1', 'Ohsumed # [ITALIC] AUC', 'Delicious # [ITALIC] F1', 'Delicious # [ITALIC] AUC'] | [['SVM', '0.629', '0.921', '0.461', '0.846'], ['L-LDA', '0.520', '0.861', '0.401', '0.763'], ['MLTM', '0.463', '0.874', '0.286', '0.780'], ['SVM [ITALIC] s', '0.418', '0.789', '0.340', '0.754'], ['L-LDA [ITALIC] s', '0.411', '0.818', '0.321', '0.745'], ['MLTM [ITALIC] s', '0.278', '0.805', '0.296', '0.781'], ['ESA', '0... | We observe that SMTM significantly outperforms all other dataless methods in terms of both Macro-F1 and Macro-AUC on both datasets. Among all dataless baselines in comparison, ESA delivers the best Macro-F1 scores on the two datasets, however with an expensive external knowledge base. We can also find that our approach... |
A Probabilistic Formulation of Unsupervised Text Style Transfer | 2002.03912 | Table 4: Comparison of gradient approximation on the sentiment transfer task. | ['[BOLD] Method', '[BOLD] train ELBO↑', '[BOLD] test ELBO↑', '[BOLD] Acc.', '[BOLD] BLEU [ITALIC] r', '[BOLD] BLEU [ITALIC] s', '[BOLD] PPLD1', '[BOLD] PPLD2'] | [['Sample-based', '-3.51', '-3.79', '87.90', '13.34', '33.19', '24.55', '25.67'], ['Greedy', '-2.05', '-2.07', '87.90', '18.67', '48.38', '27.75', '35.61']] | Greedy vs. Sample-based Gradient Approximation. In our experiments, we use greedy decoding from the inference network to approximate the expectation required by ELBO, which is a biased estimator. The main purpose of this approach is to reduce the variance of the gradient estimator during training, especially in the ear... |
A Probabilistic Formulation of Unsupervised Text Style Transfer | 2002.03912 | Table 5: Comparison of gradient propagation method on the sentiment transfer task. | ['[BOLD] Method', '[BOLD] train ELBO↑', '[BOLD] test ELBO↑', '[BOLD] Acc.', '[BOLD] BLEU [ITALIC] r', '[BOLD] BLEU [ITALIC] s', '[BOLD] PPLD1', '[BOLD] PPLD2'] | [['Gumbel Softmax', '-2.96', '-2.98', '81.30', '16.17', '40.47', '22.70', '23.88'], ['REINFORCE', '-6.07', '-6.48', '95.10', '4.08', '9.74', '6.31', '4.08'], ['Stop Gradient', '-2.05', '-2.07', '87.90', '18.67', '48.38', '27.75', '35.61']] | While being much simpler, we show that the stop-gradient trick produces superior ELBO over Gumbel Softmax and REINFORCE. This result suggests that stopping gradient helps better optimize the likelihood objective under our probabilistic formulation in comparison with other optimization techniques that propagate gradient... |
Deep Text Mining of Instagram Data Without Strong Supervision | 1909.10812 | TABLE IV: The average performance from three training runs. | ['[ITALIC] Model', '[ITALIC] Accuracy', '[ITALIC] Precision', '[ITALIC] Recall', '[ITALIC] F1'] | [['CNN-DataProgramming', '0.797±0.01', '0.566±0.05', '0.678±0.04', '0.616±0.02'], ['CNN-MajorityVote', '0.739±0.02', '0.470±0.06', '0.686±0.05', '0.555±0.03'], ['SemCluster', '0.719', '0.541', '0.453', '0.493'], ['DomainExpert', '0.807', '0.704', '0.529', '0.604']] | The Data Programming Paradigm Versus Majority The data programming approach achieves the best F1 result, on level with the human benchmark, beating both SemCluster and CNN-MajorityVote. The human benchmark had a higher precision but a lower recall than the CNN models. |
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | 1502.05698 | Table 3: Test accuracy (%) on our 20 Tasks for various methods (1000 training examples each). Our proposed extensions to MemNNs are in columns 5-9: with adaptive memory (AM), N-grams (NG), nonlinear matching function (NL), and combinations thereof. Bold numbers indicate tasks where our extensions achieve ≥95% accuracy ... | ['[EMPTY]', 'Weakly Supervised', 'Weakly Supervised', 'Uses External Resources', 'Strong Supervision (using supporting facts)', 'Strong Supervision (using supporting facts)', 'Strong Supervision (using supporting facts)', 'Strong Supervision (using supporting facts)', 'Strong Supervision (using supporting facts)', 'Str... | [['TASK~{}~{}~{}~{}~{}~{}~{}~{}~{}', 'N\\text{-}gramClassifier%', 'LSTM', 'Structured SVM% \\em COREF+SRL features', 'MemNN\\@@cite[cite]{\\@@bibref{Autho% rs Phrase1YearPhrase2}{memnns}{\\@@citephrase{(}}{\\@@citephrase{)}}}', 'MemNN\\em ADAPTIVE% MEMORY', 'MemNN\\em AM + N-% GRAMS', 'MemNN\\em AM + % NONLINEAR', 'Mem... | Learning rates and other hyperparameters for all methods are chosen using the training set. We give results for each of the 20 tasks separately, as well as mean performance and number of failed tasks in the final two rows. The adaptive approach gives a straight-forward improvement in tasks 3 and 16 because they both re... |
Conditional Self-Attention for Query-based Summarization | 2002.07338 | Table 1: Query-based summarization on Debatepedia (abstractive) and HotpotQA (extractive). Two CSA models are evaluated: (Mul) and (Add) refer to multiplicative and additive cross-attention used in CSA. | ['Model', 'Debatepedia\xa0(Nema et al., 2017 )', 'Debatepedia\xa0(Nema et al., 2017 )', 'Debatepedia\xa0(Nema et al., 2017 )', 'HotpotQA\xa0(Yang et al., 2018 )', 'HotpotQA\xa0(Yang et al., 2018 )', 'HotpotQA\xa0(Yang et al., 2018 )'] | [['Model', 'Rouge-1', 'Rouge-2', 'Rouge-L', 'Rouge-1', 'Rouge-2', 'Rouge-L'], ['Transformer\xa0Vaswani et al. ( 2017 )', '28.16', '17.48', '27.28', '35.45', '28.17', '30.31'], ['UT\xa0Dehghani et al. ( 2019 )', '36.21', '26.75', '35.53', '41.58', '32.28', '34.88'], ['SD2\xa0Nema et al. ( 2017 )', '41.26', '18.75', '40.... | Note our models have much higher Rouge-2 scores than baselines, which suggests the summarization generated by CSA is more coherent. The learned attention scores emphasize not only lexical units such as ”coal-electricity” but also conjunctive adverb such as ”therefore.” More example summaries can be found in Appendix. D... |
From Characters to Words to in Between: Do We Capture Morphology? | 1704.08352 | Table 10: Average perplexities of words that occur after reduplicated words in the test set. | ['Model', 'all', 'frequent', 'rare'] | [['word', '101.71', '91.71', '156.98'], ['characters', '[BOLD] 99.21', '[BOLD] 91.35', '[BOLD] 137.42'], ['BPE', '117.2', '108.86', '156.81']] | In contrast with the overall results, the BPE bi-LSTM model has the worst perplexities, while character bi-LSTM has the best, suggesting that these models are more effective for reduplication. |
From Characters to Words to in Between: Do We Capture Morphology? | 1704.08352 | Table 7: Perplexity results on the Czech development data, varying training data size. Perplexity using ~1M tokens annotated data is 28.83. | ['#tokens', 'word', 'char trigram', 'char'] | [['#tokens', 'word', 'bi-LSTM', 'CNN'], ['1M', '39.69', '32.34', '35.15'], ['2M', '37.59', '36.44', '35.58'], ['3M', '36.71', '35.60', '35.75'], ['4M', '35.89', '32.68', '35.93'], ['5M', '35.20', '34.80', '37.02'], ['10M', '35.60', '35.82', '39.09']] | However, we can obtain much more unannotated than annotated data, and we might guess that the character-level models would outperform those based on morphological analyses if trained on larger data. To test this, we ran experiments that varied the training data size on three representation models: word, character-trigr... |
Czech Text Document Corpus v 2.0 | 1710.02365 | Table 1: Corpus statistical information | ['Unit name Document', 'Number 11,955', 'Unit name Word', 'Number 3,505,965'] | [['Category', '60', 'Unique word', '150,899'], ['Cat. classif.', '37', 'Unique lemma', '82,986'], ['Noun', '894,951', 'Punct', '553,099'], ['Adjective', '369,172', 'Adposition', '340,785'], ['Verb', '287,253', 'Numeral', '265,430'], ['Pronoun', '258,988', 'Adverb', '144,791'], ['Coord. conj.', '100,611', 'Determiner', ... | It shows for instance that lemmatization decreases the vocabulary size from 150,899 to 82,986 which represents the reduction by 45%. Another interesting observation is the distribution of the POS tags in this corpus. |
Windowing Models for Abstractive Summarization of Long Texts | 2004.03324 | Table 1: Results on the CNN/Dailymail test set: summaries of Ty=125 tokens; Stan trained with fixed-size input of Tx=400 tokens; SWM (d=1.2, k=0.8) & DWM trained on Tx=1160 tokens, with windows of Tw=400 tokens (stride ss=380). | ['Model', 'R-1', 'R-2', 'R-L'] | [['Lead-3', '39.89', '17.22', '36.08'], ['Stan', '37.85', '16.48', '34.95'], ['SWM', '37.11', '16.01', '34.37'], ['DWM', '36.02', '15.67', '33.28']] | Unsurprisingly, the simple Lead-3 baseline outperforms Stan and both our static and dynamic windowing models. This is because in CNN/Dailymail documents almost all of the summary-relevant content is found at the very beginning of the document. The ability to process all windows does not benefit to SWM and DWM in this s... |
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