Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering


  • Zihan Xu Tsinghua University
  • Hai-Tao Zheng Tsinghua University
  • Shaopeng Zhai Shanghai Jiao Tong University
  • Dong Wang Tsinghua University



Semantic matching is a basic problem in natural language processing, but it is far from solved because of the differences between the pairs for matching. In question answering (QA), answer selection (AS) is a popular semantic matching task, usually reformulated as a paraphrase identification (PI) problem. However, QA is different from PI because the question and the answer are not synonymous sentences and not strictly comparable. In this work, a novel knowledge and cross-pair pattern guided semantic matching system (KCG) is proposed, which considers both knowledge and pattern conditions for QA. We apply explicit cross-pair matching based on Graph Convolutional Network (GCN) to help KCG recognize general domain-independent Q-to-A patterns better. And with the incorporation of domain-specific information from knowledge bases (KB), KCG is able to capture and explore various relations within Q-A pairs. Experiments show that KCG is robust against the diversity of Q-A pairs and outperforms the state-of-the-art systems on different answer selection tasks.




How to Cite

Xu, Z., Zheng, H.-T., Zhai, S., & Wang, D. (2020). Knowledge and Cross-Pair Pattern Guided Semantic Matching for Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9370-9377.



AAAI Technical Track: Natural Language Processing