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Convolutional Neural Network Architectures for Matching Natural Language Sentences

arXiv 1503.03244 ↗First posted 11 Mar 2015cs.CL cs.LG cs.NEcited by 82

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Abstract (arXiv)

Semantic matching is of central importance to many natural language tasks citebordes2014semantic,RetrievalQA. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2015.

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Citing papers per year: 2021: 30; 2022: 18; 2023: 15; 2024: 10; 2025: 8; 2026: 1.

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  6. 2021Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Taskscited by 22
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  11. 2021SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graphcited by 16
  12. 2021Learning Fine-grained Fact-Article Correspondence in Legal Casescited by 15
38 more
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  4. 2021Content Selection Network for Document-grounded Retrieval-based Chatbotscited by 10
  5. 2022Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learningcited by 10
  6. 2021Graph-based Multilingual Product Retrieval in E-commerce Searchcited by 9
  7. 2022Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intentscited by 9
  8. 2024Explaining Text Similarity in Transformer Modelscited by 9
  9. 2021Differentially Private n-gram Extractioncited by 8
  10. 2023P5: Plug-and-Play Persona Prompting for Personalized Response Selectioncited by 8
  11. 2025StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matchingcited by 8
  12. 2021Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graphcited by 7
  13. 2024CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Modelscited by 6
  14. 2021Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matchingcited by 5
  15. 2022Diagnosing BERT with Retrieval Heuristicscited by 5
  16. 2022From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Rankingcited by 5
  17. 2024Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Promptingcited by 5
  18. 2021GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyphcited by 4
  19. 2021Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approachcited by 4
  20. 2021Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learningcited by 4
  21. 2023Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraintcited by 4
  22. 2023Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learningcited by 4
  23. 2024Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commercecited by 4
  24. 2021BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matchingcited by 3
  25. 2021VIRT: Improving Representation-based Models for Text Matching through Virtual Interactioncited by 3
  26. 2023RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithmcited by 3
  27. 2024Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learningcited by 3
  28. 2021Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classificationcited by 2
  29. 2021THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responsescited by 2
  30. 2021Self-supervised Answer Retrieval on Clinical Notescited by 2
  31. 2021Embracing Structure in Data for Billion-Scale Semantic Product Searchcited by 2
  32. 2022Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metricscited by 2
  33. 2023Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichmentcited by 2
  34. 2023Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representationscited by 2
  35. 2023SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Searchcited by 2
  36. 2023Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphscited by 2
  37. 2023Low Resource Summarization using Pre-trained Language Modelscited by 2
  38. 2026RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systemscited by 2

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