The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A large annotated corpus for learning natural language inference
Open accessAbstract
Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations.However, machine learning research in this area has been dramatically limited by the lack of large-scale resources.To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning.At 570K pairs, it is two orders of magnitude larger than all other resources of its type.This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.
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“This level of expert disagreement is in line with what is observed in other well-known machine learning benchmarks59–62.”
From Humanity's Last Exam (2025) · cites this paper as [62]
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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 Bowman et al., 2015
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“For natural language inference (NLI) tasks, on most datasets, such as on RTE [116] and SNLI [16], fine-tuned models perform better than LLMs, while on some data such as CB [115], LLMs have obtained comparable performance with fine-tuned models [25].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [16]
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Flagged first, then by citations
Research works printed but not linked: 6
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“The minor decline in reasoning attribution on e-SNLI is not significant, while the decline in selection precision on PrivacyQA is—though the corresponding confidence interval for mean change in thematic drift ( [+0 .”
Influential
Metadata
- Authors
- Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Published
- 2015
- Pages
- 632-642
- OpenAlex
- W1840435438
- Cited by
- 3,377
- References
- 30
- Citations by year
- 2026: 89 · 2025: 172 · 2024: 219 · 2023: 564 · 2022: 488 · 2021: 596 · 2020: 499 · 2019: 390 · 2018: 227 · 2017: 87 · 2016: 45 · 2015: 1
- FWCI
- 67.61
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1159679)
- Defense Advanced Research Projects Agency (FA8750)
- Advanced Research Projects Agency
- U.S. Navy
- Office of Naval Research (N00014)
- Air Force Research Laboratory
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- natural language inference, logical consequence, contradiction, annotated corpus, sentence pairs, image captioning, neural network model, semantic representation
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