mlsanityData to 14 Oct 2026

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

arXiv 1512.05193 ↗First posted 16 Dec 2015cs.CLcited by 35

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Abstract (arXiv)

How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence's representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNN achieves state-of-the-art performance on AS, PI and TE tasks.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2015.

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Citing papers per year: 2021: 17; 2022: 8; 2023: 4; 2024: 1; 2025: 4; 2026: 1.

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  2. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
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  10. 2021An Explainable CNN Approach for Medical Codes Prediction from Clinical Textcited by 9
  11. 2023Spectrum-driven Mixed-frequency Network for Hyperspectral Salient Object Detectioncited by 9
  12. 2021Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledgecited by 7
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  4. 2021TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendationcited by 4
  5. 2023Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learningcited by 4
  6. 2021BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matchingcited by 3
  7. 2021A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collectionscited by 2
  8. 2022MGA-VQA: Multi-Granularity Alignment for Visual Question Answeringcited by 2
  9. 2024Towards Flexible Evaluation for Generative Visual Question Answeringcited by 2
  10. 2021A Novel Deep Learning Method for Textual Sentiment Analysiscited by 1
  11. 2021A Multi-Size Neural Network with Attention Mechanism for Answer Selectioncited by 1
  12. 2021Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolutioncited by 1
  13. 2022EMS: Efficient and Effective Massively Multilingual Sentence Embedding Learningcited by 1
  14. 2023SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classificationcited by 1
  15. 2025M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settingscited by 1
  16. 2025HalluGen: Synthesizing Realistic and Controllable Hallucinations for Evaluating Image Restorationcited by 1
  17. 2021Hierarchical Ranking for Answer Selectioncited by 0
  18. 2021SStaGCN: Simplified stacking based graph convolutional networkscited by 0
  19. 2022Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variablescited by 0
  20. 2023Reranking Passages with Coarse-to-Fine Neural Retriever Enhanced by List-Context Informationcited by 0
  21. 2025How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matchingcited by 0
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  23. 2026R-DEIM Net: An Efficient Rationale-Augmented Dual-Expert Interaction Model for Paraphrase Detectioncited by 0