Papers › A Decomposable Attention Model for Natural Language Inference
A Decomposable Attention Model for Natural Language Inference
Ankur P. Parikh, Oscar Täckström, Dipanjan Das, Jakob Uszkoreit
We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | 200D decomposable attention feed-forward model with intra-sentence attention | % Test Accuracy | 86.8 | #48 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention feed-forward model with intra-sentence attention | % Train Accuracy | 90.5 | #48 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention feed-forward model with intra-sentence attention | Parameters | 580k | #48 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention model with intra-sentence attention | % Test Accuracy | 86.8 | #49 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention model with intra-sentence attention | % Train Accuracy | 90.5 | #49 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention model with intra-sentence attention | Parameters | 580k | #49 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention feed-forward model | % Test Accuracy | 86.3 | #58 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention feed-forward model | % Train Accuracy | 89.5 | #58 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention feed-forward model | Parameters | 380k | #58 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention model | % Test Accuracy | 86.3 | #59 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention model | % Train Accuracy | 89.5 | #59 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 200D decomposable attention model | Parameters | 380k | #59 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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