Datasets:

ozdentarikcan
/

conference-slide-decks

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T001_hallucination-in-large-foundation-models	tutorial	LREC	2,026	main	null	Hallucination in Large Foundation Models	null	6	null	43	true	tutorials/T001_hallucination-in-large-foundation-models/	multideck_dataset	https://vr25.github.io/lrec26-hallucination-tutorial/
T002_eye-tracking-and-nlp	tutorial	ACL	2,025	main	null	Eye Tracking and NLP	null	1	null	82	true	tutorials/T002_eye-tracking-and-nlp/	multideck_dataset	https://acl2025-eyetracking-and-nlp.github.io/slides/Eye%20Tracking%20and%20NLP%20ACL%202025%20Tutorial%20no%20builds.pdf
T003_guardrails-and-security-for-large-language-models	tutorial	ACL	2,025	main	null	Guardrails and Security for Large Language Models	null	7	null	125	true	tutorials/T003_guardrails-and-security-for-large-language-models/	multideck_dataset	https://llm-guardrails-security.github.io/
T004_navigating-ethical-challenges-in-nlp	tutorial	ACL	2,025	main	null	Navigating Ethical Challenges in NLP	null	3	null	6	true	tutorials/T004_navigating-ethical-challenges-in-nlp/	multideck_dataset	https://ethics.aclweb.org/tutorials/ACL_2025/
T008_vulnerabilities-of-large-language-models-to-adversarial-atta	tutorial	ACL	2,024	main	null	Vulnerabilities of Large Language Models to Adversarial Attacks	null	8	null	84	true	tutorials/T008_vulnerabilities-of-large-language-models-to-adversarial-atta/	multideck_dataset	https://llm-vulnerability.github.io/
T009_computational-modeling-of-semantic-change	tutorial	EACL	2,024	main	null	Computational modeling of semantic change	null	10	null	91	true	tutorials/T009_computational-modeling-of-semantic-change/	multideck_dataset	https://www.changeiskey.org/event/2024-eacl-tutorial/
T011_synthesizing-language-and-molecules-for-scientific-insight-a	tutorial	EACL	2,024	main	null	Synthesizing Language and Molecules for Scientific Insight and Discovery	null	1	null	44	true	tutorials/T011_synthesizing-language-and-molecules-for-scientific-insight-a/	multideck_dataset	https://github.com/language-plus-molecules/LPM-24-Tutorial/blob/main/tutorial_slides.pdf
T012_transformer-specific-interpretability	tutorial	EACL	2,024	main	null	Transformer-specific Interpretability	null	3	null	43	true	tutorials/T012_transformer-specific-interpretability/	multideck_dataset	https://github.com/interpretingdl/eacl2024_transformer_interpretability_tutorial
T013_language-agents-foundations-prospects-and-risks	tutorial	EMNLP	2,024	main	null	Language Agents: Foundations, Prospects, and Risks	null	6	null	87	true	tutorials/T013_language-agents-foundations-prospects-and-risks/	multideck_dataset	https://language-agent-tutorial.github.io/
T014_formal-semantic-controls-over-language-models	tutorial	LREC	2,024	main	null	Formal Semantic Controls over Language Models	null	1	null	52	true	tutorials/T014_formal-semantic-controls-over-language-models/	multideck_dataset	https://danilosc.com/events/tutorial-lrec-2024/
T017_nlp-for-chemistry-introduction-and-recent-advances	tutorial	LREC	2,024	main	null	NLP for Chemistry – Introduction and Recent Advances	null	4	null	33	true	tutorials/T017_nlp-for-chemistry-introduction-and-recent-advances/	multideck_dataset	https://github.com/camilothorne/nlp-4-chemistry-lrec-2024
T018_navigating-the-modern-evaluation-landscape-considerations-in	tutorial	LREC	2,024	main	null	Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs)	null	7	null	91	true	tutorials/T018_navigating-the-modern-evaluation-landscape-considerations-in/	multideck_dataset	https://github.com/borgr/tutEval
T019_towards-a-human-computer-collaborative-scientific-paper-life	tutorial	LREC	2,024	main	null	Towards a Human-Computer Collaborative Scientific Paper Lifecycle: A Pilot Study and Hands-On Tutorial	null	1	null	128	true	tutorials/T019_towards-a-human-computer-collaborative-scientific-paper-life/	multideck_dataset	https://sites.google.com/view/coling2024-paper-lifecycle/
T020_tutorial-proposal-hallucination-in-large-language-models	tutorial	LREC	2,024	main	null	Tutorial Proposal: Hallucination in Large Language Models	null	4	null	40	true	tutorials/T020_tutorial-proposal-hallucination-in-large-language-models/	multideck_dataset	https://vr25.github.io/lrec-coling-hallucination-tutorial/
T021_combating-security-and-privacy-issues-in-the-era-of-large-la	tutorial	NAACL	2,024	main	null	Combating Security and Privacy Issues in the Era of Large Language Models	null	5	null	94	true	tutorials/T021_combating-security-and-privacy-issues-in-the-era-of-large-la/	multideck_dataset	https://luka-group.github.io/tutorials/tutorial.202406.html
T022_explanation-in-the-era-of-large-language-models	tutorial	NAACL	2,024	main	null	Explanation in the Era of Large Language Models	null	5	null	105	true	tutorials/T022_explanation-in-the-era-of-large-language-models/	multideck_dataset	https://explanation-llm.github.io/
T023_human-ai-interaction-in-the-age-of-llms	tutorial	EMNLP	2,023	main	null	Human-AI Interaction in the Age of LLMs	null	1	null	45	true	tutorials/T023_human-ai-interaction-in-the-age-of-llms/	multideck_dataset	https://naacl-hai.github.io/
T024_knowledge-driven-vision-language-pretraining	tutorial	AAAI	2,023	main	null	Knowledge-Driven Vision-Language Pretraining	null	5	null	78	true	tutorials/T024_knowledge-driven-vision-language-pretraining/	multideck_dataset	https://blender.cs.illinois.edu/tutorial/knowledgeVLP/
T025_complex-reasoning-in-natural-language	tutorial	ACL	2,023	main	null	Complex Reasoning in Natural Language	null	7	null	183	true	tutorials/T025_complex-reasoning-in-natural-language/	multideck_dataset	https://wenting-zhao.github.io/complex-reasoning-tutorial/
T026_goal-awareness-for-conversational-ai-proactivity-non-collabo	tutorial	ACL	2,023	main	null	Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond	null	1	null	80	true	tutorials/T026_goal-awareness-for-conversational-ai-proactivity-non-collabo/	multideck_dataset	https://dengyang17.github.io/files/ACL2023-Tutorial-ConvAI.pdf
T027_indirectly-supervised-natural-language-processing	tutorial	ACL	2,023	main	null	Indirectly Supervised Natural Language Processing	null	6	null	67	true	tutorials/T027_indirectly-supervised-natural-language-processing/	multideck_dataset	https://cogcomp.seas.upenn.edu/page/tutorial.202307/
T028_retrieval-based-language-models-and-applications	tutorial	ACL	2,023	main	null	Retrieval-based Language Models and Applications	null	7	null	108	true	tutorials/T028_retrieval-based-language-models-and-applications/	multideck_dataset	https://acl2023-retrieval-lm.github.io/
T029_denoising-diffusion-models-a-generative-learning-big-bang	tutorial	CVPR	2,023	main	null	Denoising Diffusion Models: A Generative Learning Big Bang	null	3	null	152	true	tutorials/T029_denoising-diffusion-models-a-generative-learning-big-bang/	multideck_dataset	https://cvpr2023-tutorial-diffusion-models.github.io/
T032_summarization-of-dialogues-and-conversations-at-scale	tutorial	EACL	2,023	main	null	Summarization of Dialogues and Conversations At Scale	null	1	null	46	true	tutorials/T032_summarization-of-dialogues-and-conversations-at-scale/	multideck_dataset	https://saltlab.stanford.edu/resources/
T033_understanding-ethics-in-nlp-authoring-and-reviewing	tutorial	EACL	2,023	main	null	Understanding Ethics in NLP Authoring and Reviewing	null	1	null	4	true	tutorials/T033_understanding-ethics-in-nlp-authoring-and-reviewing/	multideck_dataset	https://github.com/acl-org/ethics-tutorial
T034_designing-evaluating-and-learning-from-humans-interacting-wi	tutorial	EMNLP	2,023	main	null	Designing, Evaluating, and Learning from Humans Interacting with NLP Models	null	1	null	45	true	tutorials/T034_designing-evaluating-and-learning-from-humans-interacting-wi/	multideck_dataset	https://saltlab.stanford.edu/resources/
T035_llm-driven-instruction-following-progresses-and-concerns	tutorial	EMNLP	2,023	main	null	LLM-driven Instruction Following: Progresses and Concerns	null	6	null	106	true	tutorials/T035_llm-driven-instruction-following-progresses-and-concerns/	multideck_dataset	https://www.wenpengyin.org/publications/instruction-following-emnlp23
T036_security-challenges-in-natural-language-processing-models	tutorial	EMNLP	2,023	main	null	Security Challenges in Natural Language Processing Models	null	3	null	46	true	tutorials/T036_security-challenges-in-natural-language-processing-models/	multideck_dataset	https://emnlp2023-nlp-security.github.io/
T037_tutorial-on-multimodal-machine-learning	tutorial	ICML	2,023	main	null	Tutorial on Multimodal Machine Learning	null	7	null	77	true	tutorials/T037_tutorial-on-multimodal-machine-learning/	multideck_dataset	https://cmu-multicomp-lab.github.io/mmml-tutorial/icml2023/
T038_current-status-of-nlp-in-south-east-asia-with-insights-from	tutorial	IJCNLP, AACL	2,023	main	null	Current Status of NLP in South East Asia with Insights from Multilingualism and Language Diversity	null	1	null	54	true	tutorials/T038_current-status-of-nlp-in-south-east-asia-with-insights-from/	multideck_dataset	https://aacl2023-sea-nlp.github.io/
T040_learning-who-saying-what-to-whom-in-multi-party-conversation	tutorial	IJCNLP, AACL	2,023	main	null	Learning WHO Saying WHAT to WHOM in Multi-Party Conversations	null	6	null	35	true	tutorials/T040_learning-who-saying-what-to-whom-in-multi-party-conversation/	multideck_dataset	https://jasonforjoy.github.io/
T041_efficient-and-robust-knowledge-graph-construction	tutorial	AACL, IJCNLP	2,022	main	null	Efficient and Robust Knowledge Graph Construction	null	4	null	86	true	tutorials/T041_efficient-and-robust-knowledge-graph-construction/	multideck_dataset	https://github.com/NLP-Tutorials/AACL-IJCNLP2022-KGC-Tutorial
T043_when-cantonese-nlp-meets-pre-training-progress-and-challenge	tutorial	AACL, IJCNLP	2,022	main	null	When Cantonese NLP Meets Pre-training: Progress and Challenges	null	1	null	43	true	tutorials/T043_when-cantonese-nlp-meets-pre-training-progress-and-challenge/	multideck_dataset	https://www4.comp.polyu.edu.hk/~jing1li/talks/aacl2022-can-pretrain/Slides.pdf
T044_knowledge-augmented-methods-for-natural-language-processing	tutorial	ACL	2,022	main	null	Knowledge-Augmented Methods for Natural Language Processing	null	5	null	75	true	tutorials/T044_knowledge-augmented-methods-for-natural-language-processing/	multideck_dataset	https://github.com/zcgzcgzcg1/ACL2022_KnowledgeNLP_Tutorial
T045_learning-with-limited-text-data	tutorial	ACL	2,022	main	null	Learning with Limited Text Data	null	1	null	26	true	tutorials/T045_learning-with-limited-text-data/	multideck_dataset	https://saltlab.stanford.edu/resources/
T047_non-autoregressive-sequence-generation	tutorial	ACL	2,022	main	null	Non-Autoregressive Sequence Generation	null	4	null	41	true	tutorials/T047_non-autoregressive-sequence-generation/	multideck_dataset	https://nar-tutorial.github.io/acl2022/
T048_towards-reproducible-machine-learning-research-in-natural-la	tutorial	ACL	2,022	main	null	Towards Reproducible Machine Learning Research in Natural Language Processing	null	4	null	13	true	tutorials/T048_towards-reproducible-machine-learning-research-in-natural-la/	multideck_dataset	https://acl-reproducibility-tutorial.github.io/
T049_zero-and-few-shot-nlp-with-pretrained-language-models	tutorial	ACL	2,022	main	null	Zero- and Few-Shot NLP with Pretrained Language Models	null	7	null	111	true	tutorials/T049_zero-and-few-shot-nlp-with-pretrained-language-models/	multideck_dataset	https://github.com/allenai/acl2022-zerofewshot-tutorial
T053_non-autoregressive-models-for-fast-sequence-generation	tutorial	EMNLP	2,022	main	null	Non-Autoregressive Models for Fast Sequence Generation	null	4	null	40	true	tutorials/T053_non-autoregressive-models-for-fast-sequence-generation/	multideck_dataset	https://nar-tutorial.github.io/acl2022/
T054_contrastive-data-and-learning-for-natural-language-processin	tutorial	NAACL	2,022	main	null	Contrastive Data and Learning for Natural Language Processing	null	3	null	96	true	tutorials/T054_contrastive-data-and-learning-for-natural-language-processin/	multideck_dataset	https://contrastive-nlp-tutorial.github.io/
T055_new-frontiers-of-information-extraction	tutorial	NAACL	2,022	main	null	New Frontiers of Information Extraction	null	1	null	27	true	tutorials/T055_new-frontiers-of-information-extraction/	multideck_dataset	https://luka-group.github.io/talks.html
T059_event-centric-natural-language-processing	tutorial	ACL, IJCNLP	2,021	main	null	Event-Centric Natural Language Processing	null	6	null	130	true	tutorials/T059_event-centric-natural-language-processing/	multideck_dataset	https://cogcomp.seas.upenn.edu/page/tutorial.202108/
T060_meta-learning-and-its-applications-to-natural-language-proce	tutorial	ACL, IJCNLP	2,021	main	null	Meta Learning and Its Applications to Natural Language Processing	null	3	null	103	true	tutorials/T060_meta-learning-and-its-applications-to-natural-language-proce/	multideck_dataset	https://swdanielli.github.io/
T061_pre-training-methods-for-neural-machine-translation	tutorial	ACL, IJCNLP	2,021	main	null	Pre-training Methods for Neural Machine Translation	null	4	null	81	true	tutorials/T061_pre-training-methods-for-neural-machine-translation/	multideck_dataset	https://lileicc.github.io/TALKS/2021-ACL/
T064_advances-and-challenges-in-unsupervised-neural-machine-trans	tutorial	EACL	2,021	main	null	Advances and Challenges in Unsupervised Neural Machine Translation	null	1	null	35	true	tutorials/T064_advances-and-challenges-in-unsupervised-neural-machine-trans/	multideck_dataset	https://2021.eacl.org/downloads/tutorials/Advances-in-UNMT.pdf
T066_reviewing-natural-language-processing-research	tutorial	EACL	2,021	main	null	Reviewing Natural Language Processing Research	null	7	null	47	true	tutorials/T066_reviewing-natural-language-processing-research/	multideck_dataset	https://github.com/reviewingNLP/ACL2020T3material
T067_tutorial-end-to-end-speech-translation	tutorial	EACL	2,021	main	null	Tutorial: End-to-End Speech Translation	null	1	null	76	true	tutorials/T067_tutorial-end-to-end-speech-translation/	multideck_dataset	https://2021.eacl.org/downloads/tutorials/End-to-end-ST.pdf
T068_unsupervised-natural-language-parsing-introductory-tutorial	tutorial	EACL	2,021	main	null	Unsupervised Natural Language Parsing (Introductory Tutorial)	null	5	null	86	true	tutorials/T068_unsupervised-natural-language-parsing-introductory-tutorial/	multideck_dataset	https://github.com/tukw/unsupervised-parsing-tutorial
T069_crowdsourcing-beyond-annotation-case-studies-in-benchmark-da	tutorial	EMNLP	2,021	main	null	Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection	null	6	null	48	true	tutorials/T069_crowdsourcing-beyond-annotation-case-studies-in-benchmark-da/	multideck_dataset	https://nlp-crowdsourcing.github.io/
T070_financial-opinion-mining	tutorial	EMNLP	2,021	main	null	Financial Opinion Mining	null	1	null	33	true	tutorials/T070_financial-opinion-mining/	multideck_dataset	https://sites.google.com/nlg.csie.ntu.edu.tw/financial-opinion-mining/material
T071_knowledge-enriched-natural-language-generation	tutorial	EMNLP	2,021	main	null	Knowledge-Enriched Natural Language Generation	null	6	null	59	true	tutorials/T071_knowledge-enriched-natural-language-generation/	multideck_dataset	https://kenlg-tutorial.github.io/
T072_beyond-paragraphs-nlp-for-long-sequences	tutorial	NAACL	2,021	main	null	Beyond Paragraphs: NLP for Long Sequences	null	6	null	80	true	tutorials/T072_beyond-paragraphs-nlp-for-long-sequences/	multideck_dataset	https://github.com/allenai/naacl2021-longdoc-tutorial
T074_fine-grained-interpretation-and-causation-analysis-in-deep-n	tutorial	NAACL	2,021	main	null	Fine-grained Interpretation and Causation Analysis in Deep NLP Models	null	1	null	38	true	tutorials/T074_fine-grained-interpretation-and-causation-analysis-in-deep-n/	multideck_dataset	https://github.com/hsajjad/Interpretability-Tutorial-NAACL2021
T080_open-domain-question-answering	tutorial	ACL	2,020	main	null	Open-Domain Question Answering	null	7	null	80	true	tutorials/T080_open-domain-question-answering/	multideck_dataset	https://github.com/danqi/acl2020-openqa-tutorial
T081_reviewing-natural-language-processing-research	tutorial	ACL	2,020	main	null	Reviewing Natural Language Processing Research	null	1	null	17	true	tutorials/T081_reviewing-natural-language-processing-research/	multideck_dataset	https://github.com/reviewingNLP/ACL2020T3material
T082_a-crash-course-in-automatic-grammatical-error-correction	tutorial	COLING	2,020	main	null	A Crash Course in Automatic Grammatical Error Correction	null	5	null	112	true	tutorials/T082_a-crash-course-in-automatic-grammatical-error-correction/	multideck_dataset	https://github.com/grammatical/coling2020-tutorial
T084_cross-lingual-semantic-representation-for-nlp-with-ucca	tutorial	COLING	2,020	main	null	Cross-lingual Semantic Representation for NLP with UCCA	null	6	null	34	true	tutorials/T084_cross-lingual-semantic-representation-for-nlp-with-ucca/	multideck_dataset	https://github.com/UniversalConceptualCognitiveAnnotation/tutorial
T085_fact-checking-fake-news-propaganda-and-media-bias-truth-seek	tutorial	EMNLP	2,020	main	null	Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era	null	1	null	100	true	tutorials/T085_fact-checking-fake-news-propaganda-and-media-bias-truth-seek/	multideck_dataset	https://propaganda.math.unipd.it/emnlp20-tutorial/
T087_representation-learning-and-reasoning-on-spatial-language-fo	tutorial	EMNLP	2,020	main	null	Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks	null	3	null	99	true	tutorials/T087_representation-learning-and-reasoning-on-spatial-language-fo/	multideck_dataset	https://spatial-language-tutorial.github.io/EMNLP-2020Tutorial.html
T088_the-amazing-world-of-neural-language-generation	tutorial	EMNLP	2,020	main	null	The Amazing World of Neural Language Generation	null	5	null	120	true	tutorials/T088_the-amazing-world-of-neural-language-generation/	multideck_dataset	https://nlg-world.github.io/
T089_computational-analysis-of-political-texts-bridging-research	tutorial	ACL	2,019	main	null	Computational Analysis of Political Texts: Bridging Research Efforts Across Communities	null	1	null	99	true	tutorials/T089_computational-analysis-of-political-texts-bridging-research/	multideck_dataset	https://poltexttutorial.wordpress.com/slides/
T090_compositional-distributional-models-of-meaning	tutorial	COLING	2,016	main	null	Compositional Distributional Models of Meaning	null	1	null	24	true	tutorials/T090_compositional-distributional-models-of-meaning/	multideck_dataset	https://coling2016.anlp.jp/tutorials/T1/
acl-2015-workshop-wfirst-workshop-on-noisy-are-minority-dialects-noisy-text-implications	invited_talk	ACL	2,015	workshop	First Workshop on Noisy User-generated Text (W-NUT 2015)	Are Minority Dialects "Noisy Text"?: Implications of Social and Linguistic Diversity for Social Media NLP	Brendan O'Connor	1	41	18	true	keynotes_merged/acl-2015-workshop-wfirst-workshop-on-noisy-are-minority-dialects-noisy-text-implications/	keynote_expansion_2026	https://noisy-text.github.io/2015/
acl-2015-workshop-wfirst-workshop-on-noisy-text-mining-of-social-media-going-beyond-the	invited_talk	ACL	2,015	workshop	First Workshop on Noisy User-generated Text (W-NUT 2015)	Text Mining of Social Media: Going beyond the Text and Only the Text	Tim Baldwin	1	65	22	true	keynotes_merged/acl-2015-workshop-wfirst-workshop-on-noisy-text-mining-of-social-media-going-beyond-the/	keynote_expansion_2026	https://noisy-text.github.io/2015/
acl-2016-invited_talk-invited-talk-ii-mark-steedman	invited_talk	ACL	2,016	main	null	Invited Talk II: Mark Steedman	Mark Steedman	1	71	37	true	keynotes_merged/acl-2016-invited_talk-invited-talk-ii-mark-steedman/	keynote_style_benchmark	http://homepages.inf.ed.ac.uk/steedman/papers/semantics/acl16a.pdf
acl-2017-keynote-squashing-computational-linguistics	keynote	ACL	2,017	main	null	Squashing Computational Linguistics	Noah A. Smith	1	90	20	true	keynotes_merged/acl-2017-keynote-squashing-computational-linguistics/	keynote_expansion_2026	https://nasmith.github.io/talks/2017-08-01-keynote
acl-2018-workshop-wworkshop-on-machine-read-data-driven-reading-comprehension-successes-a	invited_talk	ACL	2,018	workshop	Workshop on Machine Reading for Question Answering (MRQA)	Data driven reading comprehension: successes and limitations	Phil Blunsom	1	35	6	true	keynotes_merged/acl-2018-workshop-wworkshop-on-machine-read-data-driven-reading-comprehension-successes-a/	keynote_expansion_2026	https://mrqa.github.io/2018/
acl-2018-workshop-wworkshop-on-machine-read-multi-step-reasoning-neural-networks-for-ques	invited_talk	ACL	2,018	workshop	Workshop on Machine Reading for Question Answering (MRQA)	Multi-step reasoning neural networks for question answering	Jianfeng Gao	1	32	15	true	keynotes_merged/acl-2018-workshop-wworkshop-on-machine-read-multi-step-reasoning-neural-networks-for-ques/	keynote_expansion_2026	https://mrqa.github.io/2018/
acl-2018-workshop-wworkshop-on-machine-read-questioning-question-answering-answers	invited_talk	ACL	2,018	workshop	Workshop on Machine Reading for Question Answering (MRQA)	Questioning Question Answering Answers	Sameer Singh	1	50	7	true	keynotes_merged/acl-2018-workshop-wworkshop-on-machine-read-questioning-question-answering-answers/	keynote_expansion_2026	https://mrqa.github.io/2018/
acl-2018-workshop-wworkshop-on-machine-read-reading-and-reasoning-with-neural-program-int	invited_talk	ACL	2,018	workshop	Workshop on Machine Reading for Question Answering (MRQA)	Reading and Reasoning with Neural Program Interpreters	Sebastian Riedel	1	157	4	true	keynotes_merged/acl-2018-workshop-wworkshop-on-machine-read-reading-and-reasoning-with-neural-program-int/	keynote_expansion_2026	https://mrqa.github.io/2018/
acl-2019-workshop-wfourth-conference-on-mac-semantic-style-other-data-divergences-in-neur	invited_talk	ACL	2,019	workshop	Fourth Conference on Machine Translation (WMT19)	Semantic, Style & Other Data Divergences in Neural Machine Translation	Marine Carpuat	1	82	29	true	keynotes_merged/acl-2019-workshop-wfourth-conference-on-mac-semantic-style-other-data-divergences-in-neur/	keynote_expansion_2026	https://www.statmt.org/wmt19/
acl-2021-keynote-reliable-characterizations-of-nlp-systems-as-a-social-r	keynote	ACL	2,021	main	null	Reliable characterizations of NLP systems as a social responsibility	Christopher Potts	1	53	23	true	keynotes_merged/acl-2021-keynote-reliable-characterizations-of-nlp-systems-as-a-social-r/	keynote_expansion_2026	https://web.stanford.edu/~cgpotts/talks.html
acl-2021-workshop-wworkshop-on-interactive-reasoning-for-communicating-with-agents	invited_talk	ACL	2,021	workshop	Workshop on Interactive Learning for Natural Language Processing (InterNLP 2021)	Reasoning for Communicating with Agents	Dan Roth	1	62	21	true	keynotes_merged/acl-2021-workshop-wworkshop-on-interactive-reasoning-for-communicating-with-agents/	keynote_expansion_2026	https://www.cis.upenn.edu/~danroth/talks.html
acl-2022-invited_talk-scaling-nlp-systems-to-the-next-1000-languages	invited_talk	ACL	2,022	main	null	Scaling NLP Systems to the Next 1000 Languages	Sebastian Ruder	1	12	13	true	keynotes_merged/acl-2022-invited_talk-scaling-nlp-systems-to-the-next-1000-languages/	keynote_expansion_2026	https://www.ruder.io/talks/
acl-2022-invited_talk-the-devil-s-in-the-data-mapping-and-generating-datasets	invited_talk	ACL	2,022	main	null	The Devil's in the Data: Mapping and Generating Datasets for Robust Generalization	Swabha Swayamdipta	1	88	13	true	keynotes_merged/acl-2022-invited_talk-the-devil-s-in-the-data-mapping-and-generating-datasets/	keynote_expansion_2026	https://swabhs.com/talks/
acl-2023-workshop-w4th-workshop-on-computat-bridging-resolution-a-journey-towards-modelli	invited_talk	ACL	2,023	workshop	4th Workshop on Computational Approaches to Discourse (CODI 2023)	Bridging Resolution: A Journey Towards Modelling Referential Discourse Entities	Yufang Hou	1	63	16	true	keynotes_merged/acl-2023-workshop-w4th-workshop-on-computat-bridging-resolution-a-journey-towards-modelli/	keynote_expansion_2026	https://yufanghou.github.io/talks/
acl-2023-workshop-w8th-workshop-on-represen-contextualizing-representations-in-varied-ann	invited_talk	ACL	2,023	workshop	8th Workshop on Representation Learning for NLP (RepL4NLP 2023)	Contextualizing Representations in Varied Annotator Perspectives: The Role of Ambiguity and Subjectivity	Swabha Swayamdipta	1	38	16	true	keynotes_merged/acl-2023-workshop-w8th-workshop-on-represen-contextualizing-representations-in-varied-ann/	keynote_expansion_2026	https://swabhs.com/talks/
acl-2024-keynote-can-llms-reason-and-plan	keynote	ACL	2,024	main	null	Can LLMs Reason and Plan?	Subbarao Kambhampati	1	113	8	true	keynotes_merged/acl-2024-keynote-can-llms-reason-and-plan/	keynote_style_benchmark	https://www.dropbox.com/scl/fi/lbmbs5fnhehchbtumm4k7/ACL-Keynote-final-as-given.pdf?rlkey=qz59z8ajivypxvjgohscih91d&dl=1
acl-2024-workshop-wfourth-workshop-on-schol-chasing-high-precision-nlp-at-discount-prices	keynote	ACL	2,024	workshop	Fourth Workshop on Scholarly Document Processing (SDP 2024)	Chasing high-precision NLP at discount prices: Lessons for accelerating science	Doug Downey	1	51	3	true	keynotes_merged/acl-2024-workshop-wfourth-workshop-on-schol-chasing-high-precision-nlp-at-discount-prices/	keynote_expansion_2026	https://sdproc.org/2024/program.html
acl-2024-workshop-wfourth-workshop-on-schol-how-to-intertext-elevating-nlp-to-the-cross-d	keynote	ACL	2,024	workshop	Fourth Workshop on Scholarly Document Processing (SDP 2024)	How to InterText? Elevating NLP to the cross-document level	Iryna Gurevych	1	51	13	true	keynotes_merged/acl-2024-workshop-wfourth-workshop-on-schol-how-to-intertext-elevating-nlp-to-the-cross-d/	keynote_expansion_2026	https://sdproc.org/2024/program.html
acl-2024-workshop-wfourth-workshop-on-schol-large-language-models-as-research-assistants	keynote	ACL	2,024	workshop	Fourth Workshop on Scholarly Document Processing (SDP 2024)	Large language models as research assistants: workflows and challenges	Anna Rogers	1	62	10	true	keynotes_merged/acl-2024-workshop-wfourth-workshop-on-schol-large-language-models-as-research-assistants/	keynote_expansion_2026	https://sdproc.org/2024/program.html
acl-2025-workshop-wfifth-workshop-on-schola-towards-an-artificial-muse-for-new-ideas-in-s	invited_talk	ACL	2,025	workshop	Fifth Workshop on Scholarly Document Processing (SDP 2025)	Towards an Artificial Muse for New Ideas in Science	Mario Krenn	1	36	8	true	keynotes_merged/acl-2025-workshop-wfifth-workshop-on-schola-towards-an-artificial-muse-for-new-ideas-in-s/	keynote_expansion_2026	https://sdproc.org/2025/program.html
cvpr-2019-workshop-wactivitynet-large-scale-towards-embodied-action-understanding	invited_talk	CVPR	2,019	workshop	ActivityNet Large Scale Activity Recognition Challenge	Towards Embodied Action Understanding	Ivan Laptev	1	53	12	true	keynotes_merged/cvpr-2019-workshop-wactivitynet-large-scale-towards-embodied-action-understanding/	keynote_expansion_2026	http://activity-net.org/challenges/2019/program.html
cvpr-2019-workshop-wactivitynet-large-scale-what-are-good-representations-for-grounding-l	invited_talk	CVPR	2,019	workshop	ActivityNet Large Scale Activity Recognition Challenge	What are good representations for grounding language in vision?	Kate Saenko	1	42	10	true	keynotes_merged/cvpr-2019-workshop-wactivitynet-large-scale-what-are-good-representations-for-grounding-l/	keynote_expansion_2026	http://activity-net.org/challenges/2019/program.html
cvpr-2019-workshop-wimage-matching-local-fea-in-defense-of-local-features-for-visual-local	invited_talk	CVPR	2,019	workshop	Image Matching: Local Features & Beyond	In Defense of Local Features for Visual Localization	Torsten Sattler	1	58	12	true	keynotes_merged/cvpr-2019-workshop-wimage-matching-local-fea-in-defense-of-local-features-for-visual-local/	keynote_expansion_2026	https://image-matching-workshop.github.io/cvpr2019/
cvpr-2019-workshop-wimage-matching-local-fea-on-the-art-of-establishing-correspondence	invited_talk	CVPR	2,019	workshop	Image Matching: Local Features & Beyond	On the Art of Establishing Correspondence	Jiri Matas	1	45	19	true	keynotes_merged/cvpr-2019-workshop-wimage-matching-local-fea-on-the-art-of-establishing-correspondence/	keynote_expansion_2026	https://image-matching-workshop.github.io/cvpr2019/
cvpr-2019-workshop-wvisual-question-answerin-making-the-l-in-vqa-matter-elevating-the-role	invited_talk	CVPR	2,019	workshop	Visual Question Answering and Dialog Workshop	Making the L in VQA Matter: Elevating the Role of Language in Visual Question Answering	Christopher Manning	1	36	10	true	keynotes_merged/cvpr-2019-workshop-wvisual-question-answerin-making-the-l-in-vqa-matter-elevating-the-role/	keynote_expansion_2026	https://visualqa.org/workshop_2019.html
cvpr-2019-workshop-wvisual-question-answerin-three-challenging-research-avenues-in-languag	invited_talk	CVPR	2,019	workshop	Visual Question Answering and Dialog Workshop	Three Challenging Research Avenues (in language and vision)	Yoav Artzi	1	38	7	true	keynotes_merged/cvpr-2019-workshop-wvisual-question-answerin-three-challenging-research-avenues-in-languag/	keynote_expansion_2026	https://visualqa.org/workshop_2019.html
cvpr-2020-workshop-wactivitynet-large-scale-efficient-video-recognition	invited_talk	CVPR	2,020	workshop	ActivityNet Large Scale Activity Recognition Challenge	Efficient Video Recognition	Christoph Feichtenhofer	1	29	5	true	keynotes_merged/cvpr-2020-workshop-wactivitynet-large-scale-efficient-video-recognition/	keynote_expansion_2026	http://activity-net.org/challenges/2020/program.html
cvpr-2020-workshop-wactivitynet-large-scale-learning-visual-representations-from-internet	invited_talk	CVPR	2,020	workshop	ActivityNet Large Scale Activity Recognition Challenge	Learning visual representations: from Internet videos towards machines that see and act	Josef Sivic	1	65	14	true	keynotes_merged/cvpr-2020-workshop-wactivitynet-large-scale-learning-visual-representations-from-internet/	keynote_expansion_2026	http://activity-net.org/challenges/2020/program.html
cvpr-2020-workshop-wearthvision-large-scale-a-new-workflow-for-collaborative-machine-lear	invited_talk	CVPR	2,020	workshop	EarthVision: Large Scale Computer Vision for Remote Sensing Imagery	A New Workflow for Collaborative Machine Learning Research in Biodiversity	Serge Belongie	1	23	2	true	keynotes_merged/cvpr-2020-workshop-wearthvision-large-scale-a-new-workflow-for-collaborative-machine-lear/	keynote_expansion_2026	https://www.grss-ieee.org/wp-content/uploads/earthvision2020/program.html
cvpr-2020-workshop-wearthvision-large-scale-linking-machine-learning-and-physical-biologi	invited_talk	CVPR	2,020	workshop	EarthVision: Large Scale Computer Vision for Remote Sensing Imagery	Linking machine learning and physical-biological dynamic system modelling	Markus Reichstein	1	24	11	true	keynotes_merged/cvpr-2020-workshop-wearthvision-large-scale-linking-machine-learning-and-physical-biologi/	keynote_expansion_2026	https://www.grss-ieee.org/wp-content/uploads/earthvision2020/program.html
cvpr-2020-workshop-wearthvision-large-scale-the-contours-of-a-trillion-pixel-digital-twin	invited_talk	CVPR	2,020	workshop	EarthVision: Large Scale Computer Vision for Remote Sensing Imagery	The contours of a trillion-pixel Digital Twin Earth	Sveinung Loekken	1	15	6	true	keynotes_merged/cvpr-2020-workshop-wearthvision-large-scale-the-contours-of-a-trillion-pixel-digital-twin/	keynote_expansion_2026	https://www.grss-ieee.org/wp-content/uploads/earthvision2020/program.html
cvpr-2020-workshop-wvisual-question-answerin-benchmarking-progress-in-ai-adversarial-nli-h	invited_talk	CVPR	2,020	workshop	Visual Question Answering and Dialog Workshop	Benchmarking Progress in AI: Adversarial NLI & Hateful Memes	Douwe Kiela	1	31	8	true	keynotes_merged/cvpr-2020-workshop-wvisual-question-answerin-benchmarking-progress-in-ai-adversarial-nli-h/	keynote_expansion_2026	https://visualqa.org/workshop_2020.html
cvpr-2020-workshop-wvisual-question-answerin-scene-text-vqa-modelling-the-interplay-betwee	invited_talk	CVPR	2,020	workshop	Visual Question Answering and Dialog Workshop	Scene Text VQA: Modelling the interplay between visual and textual information	Dimosthenis Karatzas	1	49	20	true	keynotes_merged/cvpr-2020-workshop-wvisual-question-answerin-scene-text-vqa-modelling-the-interplay-betwee/	keynote_expansion_2026	https://visualqa.org/workshop_2020.html
cvpr-2021-workshop-wactivitynet-large-scale-is-space-time-attention-all-you-need-for-vide	invited_talk	CVPR	2,021	workshop	ActivityNet Large Scale Activity Recognition Challenge	Is Space-Time Attention All You Need for Video Understanding?	Gedas Bertasius	1	41	11	true	keynotes_merged/cvpr-2021-workshop-wactivitynet-large-scale-is-space-time-attention-all-you-need-for-vide/	keynote_expansion_2026	http://activity-net.org/challenges/2021/program.html
cvpr-2021-workshop-wactivitynet-large-scale-predicting-unpredictability	invited_talk	CVPR	2,021	workshop	ActivityNet Large Scale Activity Recognition Challenge	Predicting Unpredictability	Carl Vondrick	1	40	11	true	keynotes_merged/cvpr-2021-workshop-wactivitynet-large-scale-predicting-unpredictability/	keynote_expansion_2026	http://activity-net.org/challenges/2021/program.html
cvpr-2021-workshop-wactivitynet-large-scale-toward-compositional-action-recognition-using	invited_talk	CVPR	2,021	workshop	ActivityNet Large Scale Activity Recognition Challenge	Toward Compositional Action Recognition Using Semantic Scene Graphs	Huijuan Xu	1	31	3	true	keynotes_merged/cvpr-2021-workshop-wactivitynet-large-scale-toward-compositional-action-recognition-using/	keynote_expansion_2026	http://activity-net.org/challenges/2021/program.html
cvpr-2021-workshop-wactivitynet-large-scale-towards-versatile-and-powerful-multimodal-net	invited_talk	CVPR	2,021	workshop	ActivityNet Large Scale Activity Recognition Challenge	Towards Versatile and Powerful MultiModal Networks	Jean-Baptiste Alayrac	1	64	24	true	keynotes_merged/cvpr-2021-workshop-wactivitynet-large-scale-towards-versatile-and-powerful-multimodal-net/	keynote_expansion_2026	http://activity-net.org/challenges/2021/program.html
cvpr-2021-workshop-wvisual-question-answerin-connecting-vision-and-language-with-localized	invited_talk	CVPR	2,021	workshop	Visual Question Answering (VQA) Workshop	Connecting Vision and Language with Localized Narratives and Open Images	Vittorio Ferrari	1	67	14	true	keynotes_merged/cvpr-2021-workshop-wvisual-question-answerin-connecting-vision-and-language-with-localized/	keynote_expansion_2026	https://visualqa.org/workshop.html

End of preview. Expand in Data Studio

Conference Slide Decks + Reference Papers

A dataset of 322 real conference slide decks — 62 ACL-family tutorials and 260 keynote / invited talks from top ML / CV / NLP venues — each paired with a reference-paper list (references.csv) laid out for human verification.

The goal: let a human evaluator open each deck and confirm, paper by paper, which papers are actually referenced / used on the slides. Every deck's references.csv is pre-filled with candidate references and blank verification columns for the evaluator:

tutorials — from the upstream extraction (reading-list + cited-in-deck + source papers);
keynotes / invited talks — extracted from the deck's own slide text by small-model (Haiku) readers that list only papers literally written on the slides, then filtered by a guardrail that drops any candidate whose evidence/title is not actually found in the deck text.

At a glance

	Tutorials	Keynotes / invited talks	Total
Decks	62	260	322
Deck files (many decks ship multiple section/part decks)	240	261	501
Reference candidates pre-filled	1,075	3,270	4,345
Decks with ≥1 pre-filled reference	48	250	298
Decks with no references (citation-free / no list)	14	10	24

Tutorials come from multideck_dataset (ACL / EMNLP / NAACL / EACL / LREC / COLING / AACL-IJCNLP, plus a few CVPR / ICML / AAAI), 2016–2026. Keynotes / invited talks come from keynote_style_benchmark plus a 2026 expansion (keynote_expansion_2026), now merged in keynotes_merged/ (CVPR 76, NeurIPS 53, ICCV 30, ECCV 23, EMNLP 22, ICLR 21, ACL 20, ICML 15; main + workshop), 2015–2026. After a content audit and triage, mis-bundled source papers / paper-versions / foreign decks and decks marked "unrelated" were moved out (removed_from_tutorials/, removed_unrelated/), and the two original keynote source folders were consolidated into keynotes_merged/ (originals in archive/).

Layout

hf_slidedeck_dataset/
├── README.md                 ← this dataset card
├── metadata.csv              ← master index, one row per deck (the loadable `default` config)
├── dataset_index.json        ← same index, machine-readable, with slide-file lists
├── build_hf_dataset.py       ← reproducible generator
├── tutorials/<id>/
│   ├── deck/                  ← the slide file(s) (PDF/PPTX/KEY) — copied, not linked
│   ├── references.csv         ← candidate reference papers (pre-filled) + blank verify columns
│   └── metadata.json          ← per-deck metadata
└── keynotes_merged/<id>/
    ├── deck/                  ← the slide file(s) (PDF/PPTX)
    ├── references.csv         ← candidate references extracted from the slides (pre-filled)
    └── metadata.json

`references.csv` — the human-evaluation sheet

One row per candidate reference paper. Open it in any spreadsheet tool and fill the last two columns while looking at the deck.

column	filled by	meaning
`title`	auto / human	paper title
`authors`	auto / human	authors
`year`	auto / human	year
`arxiv_id`	auto / human	arXiv id if known
`link`	auto / human	download / landing link
`provenance`	auto	why we list it as a candidate (blank for human-added rows) — see below
`verified_in_deck`	HUMAN	`Y` / `N` / `partial` — is this paper actually referenced/used on the slides?
`evaluator_notes`	HUMAN	free notes; for slide-extracted keynote rows it is pre-seeded with an `[auto-evidence] …` hint the evaluator can keep or overwrite

provenance tag vocabulary (tutorials, from the upstream extraction):

reading_list#N — paper #N on the organizers' recommended reading list.
cited_llm[:role] — an LLM reader judged it cited in the deck; role ∈ {central, discussed, mentioned, reference_only}.
cited_heuristic — a string/id/author-year matcher judged it cited.
source:{backbone|major|supporting}(contributions) — the deck's content/figures are drawn from it (contributions ∈ concept / method / figure / table / results / dataset / running_example / included_in_full), including papers off the reading list.
extracted_from_slides — (keynotes / invited talks) a paper written on the slides, found by a small-model reader and confirmed present in the deck text. For these rows evaluator_notes is pre-seeded with [auto-evidence] … — the exact slide snippet (and slide number) where it was found, to speed verification; the evaluator may overwrite it. Many are author-year citations with title empty and the citation in the evidence.
benchmark_generation_seed(Gk) — (6 keynotes) the arXiv paper ArcDeck was seeded with for that style group; not a confirmed deck reference — verify like any other row.

A blank provenance means a human added the row.

`metadata.csv` — master index (loadable table)

id, category, venue, year, track, workshop, title, presenters, n_slide_files, n_pages, n_reference_candidates, references_prefilled, relative_path, origin_dataset, source_url

category ∈ {tutorial, keynote, invited_talk}. source_url is the provenance page/file the deck was fetched from.

from datasets import load_dataset
index = load_dataset("<user>/<repo>", split="train")   # the metadata.csv table

Suggested evaluation workflow

Open <category>/<id>/deck/ and read the slide file(s).
Open <category>/<id>/references.csv.
For each pre-filled row, set verified_in_deck to Y / N / partial and add notes.
Add rows for any referenced paper not already listed (leave provenance blank).
Save. metadata.csv tells you which decks start empty (references_prefilled=False).

Caveats — read before trusting a cell

Deck bundles. Some tutorial deck/ folders contain the slide deck plus appended reference-paper PDFs and/or several section decks (e.g. 00_...tutorial.pdf then 01_2022.acl-long.236.pdf). Files are copied faithfully and not auto-filtered, because a numbered file can be a genuine second section deck. metadata.json.deck_note flags multi-file decks; the evaluator confirms which files are actually slides. Some upstream decks are also known to be mismatched (wrong talk, a proposal PDF, corrupted/duplicated text) — verify.
Pre-filled ≠ verified. provenance records why a paper is a candidate (model- and text-layer judgments from the source datasets). verified_in_deck is the ground truth this dataset is being built to collect.
16 tutorials + 131 keynotes start blank (no upstream reference list). Expected, not missing data.
Not every paper has a usable link; closed-access items may have only a landing page.
Licensing / redistribution. A publicly viewable deck is not necessarily redistributable. Every deck's original source URL is preserved (metadata.json.source_url). Verify each deck's license before publishing this dataset (e.g. to the HuggingFace Hub); treat it as an access/analysis layer, not a redistribution grant. This is why license: other.

Provenance & regeneration

Built by build_hf_dataset.py from two sibling collections in this repo: multideck_dataset/ (tutorials) and keynote_style_benchmark/ (keynotes). Re-running (python3 build_hf_dataset.py) rebuilds metadata.csv, dataset_index.json and every metadata.json, and copies any missing deck files (existing files with matching size are skipped). Existing references.csv files are left untouched so evaluator annotations are never lost — pass --force to regenerate reference sheets from scratch (this discards edits).

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