Papers › Attention Boosted Sequential Inference Model
Attention Boosted Sequential Inference Model
Guanyu Li, Pengfei Zhang, Caiyan Jia
Attention mechanism has been proven effective on natural language processing. This paper proposes an attention boosted natural language inference model named aESIM by adding word attention and adaptive direction-oriented attention mechanisms to the traditional Bi-LSTM layer of natural language inference models, e.g. ESIM. This makes the inference model aESIM has the ability to effectively learn the representation of words and model the local subsentential inference between pairs of premise and hypothesis. The empirical studies on the SNLI, MultiNLI and Quora benchmarks manifest that aESIM is superior to the original ESIM model.
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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 | MultiNLI | aESIM | Matched | 73.9 | #47 of 67 | Archive leaderboard | report |
| Natural Language Inference | MultiNLI | aESIM | Mismatched | 73.9 | #47 of 67 | Archive leaderboard | report |
| Natural Language Inference | Quora Question Pairs | aESIM | Accuracy | 88.01 | #1 of 1 | Archive leaderboard | report |
| Natural Language Inference | SNLI | aESIM | % Test Accuracy | 88.1 | #39 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.
Methods
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