Papers › Multiway Attention Networks for Modeling Sentence Pairs
Multiway Attention Networks for Modeling Sentence Pairs
Chuanqi Tan, Furu Wei, Wenhui Wang, Weifeng Lv, Ming Zhou
Modeling sentence pairs plays the vital role for judging the relationship between two sentences, such as paraphrase identification, natural language inference, and answer sentence selection. Previous work achieves very promising results using neural networks with attention mechanism. In this paper, we propose the multiway attention networks which employ multiple attention functions to match sentence pairs under the matching-aggregation framework. Specifically, we design four attention functions to match words in corresponding sentences. Then, we aggregate the matching information from each function, and combine the information from all functions to obtain the final representation. Experimental results demonstrate that the proposed multiway attention networks improve the result on the Quora Question Pairs, SNLI, MultiNLI, and answer sentence selection task on the SQuAD dataset.
Code
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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 | 150D Multiway Attention Network Ensemble | % Test Accuracy | 89.4 | #18 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 150D Multiway Attention Network Ensemble | % Train Accuracy | 95.5 | #18 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 150D Multiway Attention Network Ensemble | Parameters | 58m | #18 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 150D Multiway Attention Network | % Test Accuracy | 88.3 | #37 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 150D Multiway Attention Network | % Train Accuracy | 94.5 | #37 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 150D Multiway Attention Network | Parameters | 14m | #37 of 98 | Archive leaderboard | report |
| Paraphrase Identification | Quora Question Pairs | MwAN | Accuracy | 89.12 | #19 of 31 | 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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