Papers › A large annotated corpus for learning natural language inference
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, Christopher D. Manning
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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Code Syntology ran Syntology
3 samples harvested; 3 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | + Unigram and bigram features | % Test Accuracy | 78.2 | #91 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | + Unigram and bigram features | % Train Accuracy | 99.7 | #91 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 100D LSTM encoders | % Test Accuracy | 77.6 | #92 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 100D LSTM encoders | % Train Accuracy | 84.8 | #92 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 100D LSTM encoders | Parameters | 220k | #92 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Unlexicalized features | % Test Accuracy | 50.4 | #93 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Unlexicalized features | % Train Accuracy | 49.4 | #93 of 98 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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