mlsanityData to 14 Oct 2026

Bilateral Multi-Perspective Matching for Natural Language Sentences

arXiv 1702.03814 ↗First posted 13 Feb 2017cs.AI cs.CLcited by 121

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Abstract (arXiv)

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 rightarrow Q and P leftarrow 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.

Key facts. Cited by 121 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Feb 2017.

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Citing papers per year: 2021: 22; 2022: 30; 2023: 28; 2024: 21; 2025: 18; 2026: 2.

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38 more
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  4. 2021Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Taskscited by 22
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  7. 2022Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusioncited by 18
  8. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
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  15. 2022Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictionscited by 13
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  17. 2021Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Maskscited by 12
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  28. 2024GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theorycited by 8
  29. 2024Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimizationcited by 8
  30. 2025StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matchingcited by 8
  31. 2021Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graphcited by 7
  32. 2022Hyperbolic Relevance Matching for Neural Keyphrase Extractioncited by 7
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