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A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
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Adina Williams, Nikita Nangia, Samuel Bowman. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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“NLI measures have been used to measure faithfulness in summarization, where Maynez et al. (2020) use a textual entailment classifier trained on MNLI (Williams et al., 2018) to determine if a summary contradicts a context or not.”
From SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023) · cites this paper as Williams et al., 2018
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“InferSent (Conneau et al., 2017) uses labeled data of the Stanford Natural Language Inference dataset (Bowman et al., 2015) and the MultiGenre NLI dataset (Williams et al., 2018) to train a siamese BiLSTM network with max-pooling over the output.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Williams et al., 2018
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“Most prior entailment datasets have been human-authored (Bowman et al., 2015;Williams et al., 2018), which is known to introduce artifacts (Gururangan et al., 2018), or collected in the wild (Kamoi et al., 2023), which is challenging to scale.”
From MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (2024) · cites this paper as Williams et al., 2018
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“This released mixture uses SNLI, MNLI, FEVER, SciTail, PAWS, and VitaminC (Bowman et al., 2015;Williams et al., 2018;Thorne et al., 2018;Khot et al., 2018;Zhang et al., 2019;Schuster et al., 2021), rather than the original TRUE paper's ANLI recipe.”
From The Attribution-Compression Frontier in Retrieval-Augmented Generation (2026) · cites this paper as Williams et al., 2018
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
(2014) · Cited by 33,419
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377
Self-citationAccurate unlexicalized parsing
Klein, D., & Manning, C. D. (2003) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075096.1075150 · Cited by 3,055
Young, P., Lai, A., Hodosh, M., & Hockenmaier, J. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00166 · Cited by 2,439
Research works printed but not linked: 9
- Shai Ben-David, John Blitzer, Koby Crammer, and Fer- nando Pereira. 2007. Analysis of representations for domain adaptation. In Advances in neural informa- tion processing systems. pages 137-144.
- Marie-Catherine de Marneffe, Anna N. Rafferty, and Christopher D. Manning. 2008. Finding contradic- tions in text. In Proceedings of Association for Com- putational Linguistics-08: Human Language Tech- nology. Association for Computational Linguistics, pages 1039-1047. http://www.aclweb.org/ anthology/P08-1118.
- Yaroslav Fyodorov, Yoad Winter, and Nissim Francez. 2000. A natural logic inference system. In Proceed- ings of the 2nd Workshop on Inference in Computa- tional Semantics.
- Nancy Ide and Keith Suderman. 2006. Integrating linguistic resources: The national corpus model. In Proceedings of the Fifth International Con- ference on Language Resources and Evaluation (LREC). European Language Resources Associ- ation (ELRA). http://www.aclweb.org/ anthology/L06-1138.
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hin- ton. 2012. Imagenet classification with deep convo- lutional neural networks. In Advances in Neural In- formation Processing Systems 25, pages 1097-1105.
- Catherine Macleod, Nancy Ide, and Ralph Grishman. 2000. The American National Corpus: A standard- ized resource for American English. In Conference on Language Resources and Evaluation (LREC).
- Tsendsuren Munkhdalai and Hong Yu. 2017. Neu- ral semantic encoders. In Proceedings of the 15th Conference of the European Chapter of the Associa- tion for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, pages 397-407. http://www.aclweb.org/ anthology/E17-1038.
- Nanyun Peng and Mark Dredze. 2017. Multi-task do- main adaptation for sequence tagging. In Proceed- ings of the 2nd Workshop on Representation Learn- ing for NLP. pages 91-100.
- Anna Szabolcsi. 2010. Quantification. Cambridge University Press.
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Formal Specifications from Natural LanguagePreprint
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jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
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Self-citationmT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
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ELLE: Efficient Lifelong Pre-training for Emerging Data
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Metadata
- Authors
- Williams, A., Nangia, N., & Bowman, S.
- Affiliations
- New York University (US)
- ORCID
- Williams, Adina
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 1112-1122
- OpenAlex
- W2607892599
- Cited by
- 3,342
- References
- 34
- Citations by year
- 2026: 94 · 2025: 205 · 2024: 248 · 2023: 651 · 2022: 542 · 2021: 710 · 2020: 471 · 2019: 282 · 2018: 118
- FWCI
- 161.45
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Tencent
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- sentence comprehension, inference, natural language understanding, commonsense reasoning, question answering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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