---
title: "A Broad-Coverage Challenge Corpus for Sentence"
description: "The paper addresses the task of natural language inference (NLI). 50 references, cited by 2,320 papers here."
authors: ["Adina Williams", "Nikita Nangia", "Samuel R. Bowman"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)"
source: "https://aclanthology.org/N18-1101/"
doi: "10.18653/v1/n18-1101"
arxiv: "1704.05426"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/"
---
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# A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

**Authors:** [Adina Williams](https://md.research.elamur.ai/free/en/authors/adina-williams/), [Nikita Nangia](https://md.research.elamur.ai/free/en/authors/nikita-nangia/) and [Samuel R. Bowman](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) · **Year:** 2018 · **Venue:** [Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2018](https://md.research.elamur.ai/free/en/venues/naacl/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N18-1101/) · [PDF](https://aclanthology.org/N18-1101.pdf) · [DOI 10.18653/v1/n18-1101](https://doi.org/10.18653/v1/n18-1101) · [arXiv 1704.05426](https://arxiv.org/abs/1704.05426) · [OpenAlex](https://openalex.org/W2607892599)

**Citations:** 3,231 citations (OpenAlex, 2026-10-14) · [2320](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses the task of natural language inference (NLI). A model receives a pair of sentences, a premise and a hypothesis, and must determine whether the second follows from the first, contradicts it, or is neutral with respect to it. The main resource at the time, SNLI, consisted solely of image captions. As a result, its sentences are short and simple, and the best models had already nearly reached human-level performance. The authors created MultiNLI, a corpus of 433k pairs. It was collected in almost the same way as SNLI: crowdworkers were shown a sentence from real text and wrote three new sentences for it, one for each label. The source sentences were drawn from ten genres of written and spoken American English. Only five genres were included in the training set. This makes it possible to evaluate performance on familiar genres (matched) separately from transfer to unfamiliar ones (mismatched). Models designed for SNLI achieve considerably lower accuracy on MultiNLI, even though annotator agreement is roughly the same in both corpora. The new resource is therefore more challenging, leaves ample room for progress, and is also suitable for research on domain adaptation and transfer learning.

## Key points
- **Size and labels.** 433k pairs labeled ENTAILMENT, NEUTRAL and CONTRADICTION. One hypothesis was written for each label per premise, so the classes in the raw data are balanced.
- **Sources.** Nine genres are taken from the Open American National Corpus (OANC): Face-to-face, Government, Letters, 9/11, OUP, Slate, Telephone, Travel, Verbatim. The tenth genre, Fiction, was compiled by the authors from freely available fiction published between 1912 and 2010. SNLI is not part of the corpus, but it can be added as a separate genre, CAPTIONS.
- **Data split.** All ten genres are present in dev and test, but only five in train. This yields two evaluation sections: matched and mismatched.
- **Analysis by linguistic tags.** Comparatives and superlatives occur in 17% of pairs, and on these the baseline models lose 3–4 points of accuracy. On discourse markers the loss is 2–3 points. Pairs containing negation are more often labeled CONTRADICTION. The ESIM model with attention handles sentences longer than 20 words better than the others.
- **Subsequent use.** A draft of the corpus was released in 2017. Since then, Conneau et al. (2017) have shown that training on SNLI together with MultiNLI yields sentence vector representations that transfer well to other tasks.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

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## Cite

Cite as: Adina Williams, Nikita Nangia and Samuel R. Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{williams2018broad,
  title = {{A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference}},
  author = {Adina Williams, Nikita Nangia and Samuel R. Bowman},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-1101},
  eprint = {1704.05426},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n18-1101}
}
```

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*Updated 1 November 2026*