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
The cited source says what the claim attributes to it.
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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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In the citing paper
“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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In the citing paper
“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
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1070 · Cited by 2,049
Semantics in generative grammar
Heim, I., & Kratzer, A. (1998) · Cited by 1,905
Domain adaptation with structural correspondence learning
Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543
A Decomposable Attention Model for Natural Language Inference
Parikh, A. P., Täckström, O., Das, D., & Uszkoreit, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1244 · Cited by 1,351
Enhanced LSTM for Natural Language Inference
Chen, Q., Zhu, X., Ling, Z.-H., Wei, S., Jiang, H., & Inkpen, D. Z. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1152 · Cited by 1,315
A large annotated corpus for learning natural language inferencePreprint
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.05326 · Cited by 1,020
Self-citationDomain Adaptation for Statistical Classifiers
Daume III, H., & Marcu, D. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1872 · Cited by 816
Unger, P., & Munitz, M. K. (1974) · Cited by 739
A SICK cure for the evaluation of compositional distributional semantic models
Marelli, M., Menini, S., Baroni, M., Bentivogli, L., Bernardi, R., & Zamparelli, R. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/39qdhuevzbqa · Cited by 693
The pascal recognising textual entailment challenge
Dagan, I., & Glickman, O. (2005) · Cited by 587
Learning Natural Language Inference with LSTM
Wang, S., & Jiang, J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1170 · Cited by 449
Marelli, M., Bentivogli, L., Baroni, M., Raffaella Bernardi, B., Menini, S., & Zamparelli, R. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2001 · Cited by 378
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Mou, L., Men, R., Li, G., Xu, Y., Zhang, L., Yan, R., & Jin, Z. G. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2022 · Cited by 340
How Transferable are Neural Networks in NLP Applications?
Mou, L., Zhao, M., Yan, R., Li, G., Xu, Y., Zhang, L., & Jin, Z. G. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1046 · Cited by 337
A Fast Unified Model for Parsing and Sentence Understanding
Bowman, S. R., Gauthier, J., Rastogi, A., Gupta, R., Manning, C. D., & Potts, C. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1139 · Cited by 254
Self-citationRecognising textual entailment with logical inference
Bos, J., & Markert, K. (2005) · doi:10.3115/1220575.1220654 · Cited by 231
Finding Contradictions in Text
de Marneffe, M.-C., Rafferty, A. N., & Manning, C. D. (2008) · Cited by 227
Manning, C. D., & MacCartney, B. (2009) · Cited by 209
An extended model of natural logic
MacCartney, B., & Manning, C. D. (2009) · International Conference on Computational Semantics (IWCS) · doi:10.3115/1693756.1693772 · Cited by 189
Entailment, intensionality and text understanding
Condoravdi, C. A., Crouch, D., de Paiva, V., Stolle, R., & Bobrow, D. G. (2003) · doi:10.3115/1119239.1119245 · Cited by 99
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Nangia, N., Williams, A., Lazaridou, A., & Bowman, S. R. (2017) · Workshop on Evaluating Vector-Space Representations for NLP · doi:10.18653/v1/w17-5301 · Cited by 94
Self-citationHow Transferable are Neural Networks in NLP Applications?Preprint
Mou, L., Zhao, M., Yan, R., Li, G., Xu, Y. H., Zhang, L., & Jin, Z. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06111 · Cited by 83
The semantics–pragmatics interface
Schlenker, P. (2016) · Cambridge University Press eBooks · doi:10.1017/cbo9781139236157.023 · Cited by 55
The American National Corpus: A Standardized Resource for American English
MacLeod, C. A., Ide, N., & Grishman, R. (2000) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/37awmr3fvg6d · Cited by 50
An American national corpus: a proposal
Fillmore, C. J., Ide, N., Jurafsky, D. S., & MacLeod, C. A. (1998) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2edfbyev2zpg · Cited by 30
Flagged first, then by citations
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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Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778
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Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
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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