Papers › Bilateral Multi-Perspective Matching for Natural Language Sentences
Bilateral Multi-Perspective Matching for Natural Language Sentences
Zhiguo Wang, Wael Hamza, Radu Florian
Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model under the "matching-aggregation" framework. Given two sentences P and Q, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions P →Q and P ←Q. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, the decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.
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Code Syntology ran Syntology
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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 | BiMPM Ensemble | % Test Accuracy | 88.8 | #30 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | BiMPM Ensemble | % Train Accuracy | 93.2 | #30 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | BiMPM Ensemble | Parameters | 6.4m | #30 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | BiMPM | % Test Accuracy | 87.5 | #42 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | BiMPM | % Train Accuracy | 90.9 | #42 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | BiMPM | Parameters | 1.6m | #42 of 98 | Archive leaderboard | report |
| Paraphrase Identification | Quora Question Pairs | BiMPM | Accuracy | 88.17 | #25 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.
Methods
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