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
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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.
In the citing paper
“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
The cited source says what the claim attributes to it.
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
The cited source says what the claim attributes to it.
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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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.
Cited by
Sorted by citations
Items 601–700 of 1,030
Muppet: Massive Multi-task Representations with Pre-FinetuningPreprint
Aghajanyan, A., Gupta, A., Shrivastava, A., Chen, X., Zettlemoyer, L., & Gupta, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.11038 · Cited by 65
Story Ending Prediction by Transferable BERT
Li, Z., Ding, X., & Liu, T. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/249 · Cited by 63
A Logic-Driven Framework for Consistency of Neural Models
Li, T., Gupta, V., Mehta, M., & Srikumar, V. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1405 · Cited by 64
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models
Li, L., Lei, J., Gan, Z., & Liu, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00205 · Cited by 64
Lin, B. Y., Wu, Z., Yang, Y., Lee, D., & Ren, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.131 · Cited by 66
Niksirat, K. S., Goswami, L., Rao, P. S. B., Tyler, J., Silacci, A., Aliyu, S., Aebli, A., Wacharamanotham, C., & Cherubini, M. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580848 · Cited by 64
Kim, H., Kang, H., Choi, H., Jung, D., & Hong, T. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104938 · Cited by 64
Refiner: Refining Self-attention for Vision TransformersPreprint
Zhou, D., Shi, Y., Kang, B., Yu, W., Jiang, Z., Li, Y., Jin, X., Hou, Q., & Feng, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03714 · Cited by 64
Xu, J., Tan, X., Luo, R., Song, K., Li, J., Qin, T., & Liu, T. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467262 · Cited by 63
Can NLI Models Verify QA Systems’ Predictions?
Chen, J., Choi, E., & Durrett, G. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.324 · Cited by 63
Tao, L., Zhang, H., Jing, H., Liu, Y., Yan, D., Wei, G., & Xue, X. (2025) · Remote Sensing · doi:10.3390/rs17010162 · Cited by 63
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation FrameworkPreprint
Sansford, H., Richardson, N., Maretić, H. P., & Saada, J. N. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2407.10793 · Cited by 63
Enhancing AMR-to-Text Generation with Dual Graph Representations
Ribeiro, L. F. R., Gardent, C., & Gurevych, I. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1314 · Cited by 62
Yin, W., Rajani, N. F., Radev, D., Socher, R., & Xiong, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.660 · Cited by 62
Optimised weight programming for analogue memory-based deep neural networks
Mackin, C., Rasch, M. J., Chen, A., Timcheck, J., Bruce, R. L., Li, N., Narayanan, P., Ambrogio, S., Gallo, M. L., Nandakumar, S. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31405-1 · Cited by 62
Mokoatle, M., Marivate, V. N., Mapiye, D. S., Bornman, R. M. S., & Hayes, V. M. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05235-x · Cited by 63
Word n-gram attention models for sentence similarity and inference
López-Gazpio, I., Maritxalar, M., Lapata, M., & Agirre, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.054 · Cited by 62
What is More Likely to Happen Next? Video-and-Language Future Event Prediction
Lei, J., Yu, L., Berg, T. L., & Bansal, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.706 · Cited by 63
RAFT: A Real-World Few-Shot Text Classification BenchmarkPreprint
Alex, N., Lifland, E., Tunstall, L., Thakur, A., Maham, P., Riedel, C. J., Hine, E., Ashurst, C., Sedille, P., Carlier, A., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14076 · Cited by 62
Temporal Reasoning on Implicit Events from Distant Supervision
Zhou, B., Richardson, K., Ning, Q., Khot, T., Sabharwal, A., & Roth, D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.107 · Cited by 61
Karamcheti, S., Krishna, R., Fei-Fei, L., & Manning, C. D. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.564 · Cited by 62
Open Aspect Target Sentiment Classification with Natural Language Prompts
Seoh, R., Birle, I., Tak, M., Chang, H.-S., Pinette, B., & Hough, A. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.509 · Cited by 61
Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures
Bagdasaryan, E., & Shmatikov, V. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833572 · Cited by 64
Li, B., Kong, Z., Zhang, T., Li, J., Li, Z., Liu, H., & Ding, C. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.286 · Cited by 61
TweetBERT: A Pretrained Language Representation Model for Twitter Text AnalysisPreprint
Qudar, M. M. A., & Mago, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11091 · Cited by 61
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy
Behnia, R., Ebrahimi, M. R., Pacheco, J., & Padmanabhan, B. (2022) · 2022 IEEE International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw58026.2022.00078 · Cited by 60
Revisiting Parameter-Efficient Tuning: Are We Really There Yet?
Chen, G., Liu, F., Meng, Z., & Liang, S. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.168 · Cited by 60
OpenPrompt: An Open-source Framework for Prompt-learning
Ding, N., Hu, S., Zhao, W., Chen, Y., Liu, Z., Zheng, H.-T., & Sun, M. (2021) · arXiv · doi:10.48550/arxiv.2111.01998 · Cited by 60
“And the evaluation tasks include WebNLG (Gardent et al., 2017) for conditional generation, GLUE (Wang et al., 2018) and Super-GLUE (Wang et al., 2019) for natural language understanding; SemEval (Hendrickx et al., 2010) for relation extraction; Few-NERD (Ding et al., 2021b) for fine-grained entity typing; MNLI (William…”
MethodUnderstanding by Understanding Not: Modeling Negation in Language Models
Hosseini, A., Reddy, S., Bahdanau, D., Hjelm, R. D., Sordoni, A., & Courville, A. C. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.102 · Cited by 61
NaturalProofs: Mathematical Theorem Proving in Natural LanguagePreprint
Welleck, S., Liu, J., Bras, R. J. L., Hajishirzi, H., Choi, Y., & Cho, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01112 · Cited by 60
Composing Parameter-Efficient Modules with Arithmetic OperationsPreprint
Zhang, J., Chen, S., Liu, J., & He, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.14870 · Cited by 60
aeroBERT-Classifier: Classification of Aerospace Requirements Using BERT
Ray, A. T., Cole, B. F., Fischer, O. J. P., White, R. T., & Mavris, D. N. (2023) · Aerospace · doi:10.3390/aerospace10030279 · Cited by 59
Probing Across Time: What Does RoBERTa Know and When?
Liu, Z., Wang, Y., Kasai, J., Hajishirzi, H., & Smith, N. A. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.71 · Cited by 59
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning PerspectivePreprint
Zeng, Z., Cheng, Q., Yin, Z., Wang, B., Li, S., Zhou, Y., Guo, Q., Huang, X., & Qiu, X. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2412.14135 · Cited by 61
On the Paradox of Learning to Reason from Data
Zhang, H., Li, L. H., Meng, T., Chang, K.-W., & Van den Broeck, G. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/375 · Cited by 58
On the Paradox of Learning to Reason from Data
Zhang, H., Li, L. H., Meng, T., Chang, K.-W., & Broeck, G. V. D. (2022) · arXiv · doi:10.48550/arxiv.2205.11502 · Cited by 58
Multimodal Pre-Training Based on Graph Attention Network for Document Understanding
Zhang, Z., Ma, J., Du, J., Wang, L., & Zhang, J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3214102 · Cited by 58
Fake news detection using dual BERT deep neural networks
Farokhian, M., Rafe, V., & Veisi, H. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17115-w · Cited by 58
Assessing political bias and value misalignment in generative artificial intelligence
Motoki, F. Y. S., Neto, V. P., & Rodrigues, V. R. D. S. (2025) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2025.106904 · Cited by 58
Load What You Need: Smaller Versions of Mutililingual BERT
Abdaoui, A., Pradel, C., & Sigel, G. (2020) · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) · doi:10.18653/v1/2020.sustainlp-1.16 · Cited by 58
Fu, H., Zhou, S., Yang, Q., Tang, J., Liu, G., Liu, K., & Li, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17518 · Cited by 58
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization
Zhang, X., Li, P., & Li, H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.37 · Cited by 58
DisSent: Sentence Representation Learning from Explicit Discourse RelationsPreprint
Nie, A., Bennett, E., & Goodman, N. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.04334 · Cited by 58
Explaining The Efficacy of Counterfactually Augmented DataPreprint
Kaushik, D., Setlur, A., Hovy, E. H., & Lipton, Z. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02114 · Cited by 58
Revisiting the Primacy of English in Zero-shot Cross-lingual TransferPreprint
Turc, I., Lee, K., Eisenstein, J., Chang, M.-W., & Toutanova, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.16171 · Cited by 58
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement LearningPreprint
Xi, Z., Chen, W.-X., Hong, B., Jin, S., Zheng, R., He, W., Ding, Y., Liu, S., Guo, X., Wang, J., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.05808 · Cited by 58
Targeting the Benchmark: On Methodology in Current Natural Language Processing Research
Schlangen, D. (2021) · doi:10.18653/v1/2021.acl-short.85 · Cited by 60
Universal Sentence Representation Learning with Conditional Masked Language Model
Yang, Z., Yang, Y., Cer, D. M., Law, J., & Darve, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.502 · Cited by 58
Explicit Alignment Objectives for Multilingual Bidirectional Encoders
Hu, J., Johnson, M., Fırat, O., Siddhant, A., & Neubig, G. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.284 · Cited by 57
Improved Methods to Aid Unsupervised Evidence-Based Fact Checking for Online Heath News
Deka, P., Jurek, A., & Deepak, P. (2022) · Journal of Data Intelligence · doi:10.26421/jdi3.4-5 · Cited by 58
BERT & Family Eat Word Salad: Experiments with Text Understanding
Gupta, A., Kvernadze, G., & Srikumar, V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17531 · Cited by 57
PMI-Masking: Principled masking of correlated spansPreprint
Levine, Y., Lenz, B., Lieber, O., Abend, O., Leyton‐Brown, K., Tennenholtz, M., & Shoham, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01825 · Cited by 57
Variational Information Bottleneck for Effective Low-Resource Fine-TuningPreprint
Mahabadi, R. K., Belinkov, Y., & Henderson, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05469 · Cited by 57
Teach Me to Explain: A Review of Datasets for Explainable NLP.Preprint
Wiegreffe, S., & Marasović, A. (2021) · arXiv (Cornell University) · Cited by 57
Sun, Y., Zheng, Y., Hao, C., & Qiu, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03564 · Cited by 57
A Survey on Data Augmentation in Large Model EraPreprint
Zhou, Y., Guo, C., Wang, X., Chang, Y., & Wu, Y. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.15422 · Cited by 57
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
Chen, L., Zuo, S., Chen, M., Jiang, H., Liu, X., He, P., Zhao, T., & Chen, W. (2021) · doi:10.18653/v1/2021.acl-long.510 · Cited by 58
TextNAS: A Neural Architecture Search Space Tailored for Text Representation
Wang, Y., Yang, Y., Chen, Y., Bai, J., Zhang, C., Su, G., Kou, X., Tong, Y., Yang, M., & Zhou, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6462 · Cited by 56
Zero-Shot Text Classification via Knowledge Graph Embedding for Social Media Data
Chen, Q., Wang, W., Huang, K., & Coenen, F. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3093065 · Cited by 56
AR-LSAT: Investigating Analytical Reasoning of TextPreprint
Zhong, W., Wang, S., Tang, D., Xu, Z., Guo, D., Wang, J., Yin, J. P., Zhou, M., & Duan, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06598 · Cited by 56
Discrete and Soft Prompting for Multilingual Models
Zhao, M., & Schütze, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.672 · Cited by 55
TextHide: Tackling Data Privacy in Language Understanding Tasks
Huang, Y., Song, Z., Chen, D., Li, K., & Arora, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.123 · Cited by 55
Long Document Summarization in a Low Resource Setting using Pretrained Language Models
Bajaj, A., Dangati, P., Krishna, K., Kumar, P. A., Uppaal, R., Windsor, B., Brenner, E., Dotterrer, D., Das, R., & McCallum, A. (2021) · doi:10.18653/v1/2021.acl-srw.7 · Cited by 55
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable RepresentationsPreprint
Yang, Z., Zhao, J., Dhingra, B., He, K., Cohen, W. W., Salakhutdinov, R., & LeCun, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05662 · Cited by 55
Simple data balancing achieves competitive worst-group-accuracyPreprint
Idrissi, B. Y., Arjovsky, M., Pezeshki, M., & López-Paz, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14503 · Cited by 55
Cao, H. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2406.01607 · Cited by 55
Testing AI on language comprehension tasks reveals insensitivity to underlying meaning
Dentella, V., Günther, F., Murphy, E., Marcus, G., & Leivada, E. (2024) · Scientific Reports · doi:10.1038/s41598-024-79531-8 · Cited by 54
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
Ben-David, E., Oved, N., & Reichart, R. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00468 · Cited by 54
Pairwise Supervised Contrastive Learning of Sentence Representations
Zhang, D., Li, S.-W. D., Wei, X., Zhu, H., Nallapati, R. M., Arnold, A. O., & Xiang, B. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.467 · Cited by 55
Varshney, N., Mishra, S. R., & Baral, C. R. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.158 · Cited by 54
Cho, S., Lebanoff, L., Foroosh, H., & Liu, F. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1098 · Cited by 54
Evidence Sentence Extraction for Machine Reading Comprehension
Wang, H., Yu, D., Sun, K., Chen, J., Yu, D., McAllester, D., & Roth, D. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1065 · Cited by 54
Neural Graph Matching Networks for Chinese Short Text Matching
Chen, L., Zhao, Y., Lyu, B., Jin, L., Chen, Z., Zhu, S., & Yu, K. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.547 · Cited by 54
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data
Guo, H., Tan, B., Liu, Z., Xing, E. P., & Hu, Z. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.518 · Cited by 54
Dynamic Multi-Level Multi-Task Learning for Sentence SimplificationPreprint
Guo, H., Pasunuru, R., & Bansal, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07304 · Cited by 54
Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering
Hu, J. E., Singh, A., Holzenberger, N., Post, M., & Durme, B. V. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1005 · Cited by 53
Classifying User Requirements from Online Feedback in Small Dataset Environments using Deep Learning
Mekala, R. R., Irfan, A., Groen, E. C., Porter, A., & Lindvall, M. (2021) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re51729.2021.00020 · Cited by 53
MENLI: Robust Evaluation Metrics from Natural Language Inference
Chen, Y., & Eger, S. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00576 · Cited by 53
Reasoning Like Program Executors
Pi, X., Liu, Q., Chen, B., Ziyadi, M., Lin, Z., Fu, Q., Gao, Y., Lou, J., & Chen, W. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.48 · Cited by 53
Wu, Z., Zhang, L., Cao, C., Yu, X., Liu, Z., Zhao, L., Li, Y., Dai, H., Ma, C., Li, G., et al. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3536928 · Cited by 56
Generative propaganda: Evidence of AI’s impact from a state-backed disinformation campaign
Wack, M., Ehrett, C., Linvill, D. L., & Warren, P. L. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf083 · Cited by 53
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Yanaka, H., Mineshima, K., Bekki, D., Inui, K., Sekine, S., Abzianidze, L., & Bos, J. (2019) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s19-1027 · Cited by 53
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
Seo, M., Kwiatkowski, T., Parikh, A. P., Farhadi, A., & Hajishirzi, H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1052 · Cited by 53
Sentence embeddings in NLI with iterative refinement encoders
Talman, A., Yli-Jyrä, A., & Tiedemann, J. (2019) · Natural Language Engineering · doi:10.1017/s1351324919000202 · Cited by 53
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
Chan, A., Tay, Y., Ong, Y.-S., & Zhang, A. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.373 · Cited by 53
A pragmatic and intelligent model for sarcasm detection in social media text
Shrivastava, M., & Kumar, S. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101489 · Cited by 53
Kuhn, L., Gal, Y., & Farquhar, S. (2023) · arXiv · doi:10.48550/arxiv.2302.09664 · Cited by 700
“, 2020a) that is fine-tuned on the NLI data set MNLI (Williams et al., 2017).”
BackgroundMethodJin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., & Schölkopf, B. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.532 · Cited by 52
TA-SBERT: Token Attention Sentence-BERT for Improving Sentence Representation
Seo, J., Lee, S., Liu, L., & Choi, W. (2022) · IEEE Access · doi:10.1109/access.2022.3164769 · Cited by 52
CoSENT: Consistent Sentence Embedding via Similarity Ranking
Huang, X., Peng, H., Zou, D., Liu, Z., Li, J., Liu, K., Wu, J. X., Su, J., & Yu, P. S. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3402087 · Cited by 53
Evaluating BERT for natural language inference: A case study on the CommitmentBank
Jiang, N., & de Marneffe, M.-C. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1630 · Cited by 52
QuALITY: Question Answering with Long Input Texts, Yes!
Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.391 · Cited by 51
Self-citationCollecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Poliak, A., Haldar, A., Rudinger, R., Hu, J. E., Pavlick, E., White, A. S., & Durme, B. V. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5441 · Cited by 51
A Minimax Game for Instance based Selective Transfer Learning
Wang, B., Qiu, M., Wang, X., Li, Y., Gong, Y., Zeng, X., Huang, J., Zheng, B., Cai, D., & Zhou, J. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330841 · Cited by 51
Wang, N. X. R., Mahajan, D., Danilevsky, M., & Rosenthal, S. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.39 · Cited by 51
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
Fang, T., Wang, C., Qiu, M., Zhang, Y., Li, Y., & Huang, J. (2021) · doi:10.18653/v1/2021.acl-long.236 · Cited by 51
A Compare-Propagate Architecture with Alignment Factorization for Natural Language InferencePreprint
Tay, Y., Tuan, L. A., & Hui, S. C. (2017) · arXiv (Cornell University) · Cited by 51
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkPreprint
Xu, L. J., Lu, X., Yuan, C., Zhang, X., Xu, H., Hu, Y. C., Wei, G., Pan, X., Tian, X., Qin, L., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07498 · Cited by 51
KaLM-Embedding: Superior Training Data Brings A Stronger Embedding ModelPreprint
Hu, X., Shan, Z., Zhao, X., Sun, Z., Liu, Z., Li, D., Ye, S., Wei, X., Chen, Q., Hu, B., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2501.01028 · Cited by 53
Contrastive Learning Models for Sentence Representations
Xu, L., Xie, H., Li, Z., Wang, F. L., Wang, W., & Li, Q. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3593590 · Cited by 50
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 ›
Are you an author, or do you see a problem with this page? Request a review
