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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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Chung, S., Moon, S., Kim, J.-H., Kim, J., Lim, S. P., & Chi, S. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105020 · Cited by 87
Learning to Compute Word Embeddings On the FlyPreprint
Bahdanau, D., Bosc, T., Jastrzȩbski, S., Grefenstette, E., Vincent, P., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00286 · Cited by 87
Multitask Prompt Tuning Enables Parameter-Efficient Transfer LearningPreprint
Wang, Z., Panda, R., Karlinsky, L., Feris, R., Sun, H., & Kim, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.02861 · Cited by 87
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · arXiv · doi:10.48550/arxiv.1908.10084 · Cited by 343
ScienceQA: a novel resource for question answering on scholarly articles
Saikh, T., Ghosal, T., Mittal, A., Ekbal, A., & Bhattacharyya, P. (2022) · International Journal on Digital Libraries · doi:10.1007/s00799-022-00329-y · Cited by 86
FaithDial : A Faithful Benchmark for Information-Seeking Dialogue
Dziri, N., Kamalloo, E., Milton, S., Zai͏̈ane, O. R., Yu, M., Ponti, E. M., & Reddy, S. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00529 · Cited by 86
Deep Enhanced Representation for Implicit Discourse Relation RecognitionPreprint
Bai, H., & Zhao, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.05154 · Cited by 86
InterBERT: Vision-and-Language Interaction for Multi-modal PretrainingPreprint
Lin, J., An, Y., Zhang, Y., Liu, J., Zhou, J., & Yang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13198 · Cited by 86
Jointly learning sentence embeddings and syntax with unsupervised Tree-LSTMs
Maillard, J., Clark, S. C., & Yogatama, D. (2019) · Natural Language Engineering · doi:10.1017/s1351324919000184 · Cited by 85
Li, H., Zhu, J., Zhang, J., & Zong, C. (2018) · International Conference on Computational Linguistics · Cited by 85
Adversarially Regularising Neural
Minervini, P., & Riedel, S. (2018) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k18-1007 · Cited by 85
KLEJ: Comprehensive Benchmark for Polish Language Understanding
Rybak, P., Mroczkowski, R., Tracz, J., & Gawlik, I. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.111 · Cited by 87
Short Text Clustering Algorithms, Application and Challenges: A Survey
Ahmed, M. H., Tiun, S. B., Omar, N., & Sani, N. S. (2022) · Applied Sciences · doi:10.3390/app13010342 · Cited by 85
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation
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With Little Power Comes Great Responsibility
Card, D., Henderson, P., Khandelwal, U., Jia, R., Mahowald, K., & Jurafsky, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.745 · Cited by 84
Contrastive Explanations for Model Interpretability
Jacovi, A., Swayamdipta, S., Ravfogel, S., Elazar, Y., Choi, Y., & Goldberg, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.120 · Cited by 83
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
Chen, S., Zhang, F., Sone, K., & Roth, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.475 · Cited by 83
Zero-shot Relation Classification as Textual Entailment
Obamuyide, A., & Vlachos, A. (2018) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/w18-5511 · Cited by 83
Lauscher, A., Majewska, O., Ribeiro, L. F. R., Gurevych, I., Rozanov, N., & Glavaš, G. (2020) · Workshop on Knowledge Extraction and Integration for Deep Learning Architectures: Deep Learning Inside Out (DeeLIO) · doi:10.18653/v1/2020.deelio-1.5 · Cited by 83
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
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Fake news detection based on a hybrid BERT and LightGBM models
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Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
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AlignScore: Evaluating factual consistency with a unified alignment function
Zha, Y., Yang, Y., Li, R., & Hu, Z. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.634 · Cited by 81
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
Lauscher, A., Vulić, I., Ponti, E. M., Korhonen, A., & Glavaš, G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.118 · Cited by 82
Multiple Data Augmentation Strategies for Improving Performance on Automatic Short Answer Scoring
Lun, J., Zhu, J., Tang, Y., & Yang, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i09.7062 · Cited by 82
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Zhang, J., Hashimoto, K., Liu, W., Wu, C.-S., Wan, Y., Yu, P. S., Socher, R., & Xiong, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.411 · Cited by 81
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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