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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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Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
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That is a Known Lie: Detecting Previously Fact-Checked Claims
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Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
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Multilingual Alignment of Contextual Word RepresentationsPreprint
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THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
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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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