The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
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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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
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