Deep Learning Approaches to Semantic Relevance Modeling for Chinese Question-Answer Pairs

  • Authors:

  • Baoxun Wang;Bingquan Liu;Xiaolong Wang;Chengjie Sun;Deyuan Zhang

  • Affiliations:

  • Harbin Institute of Technology;Harbin Institute of Technology;Harbin Institute of Technology;Harbin Institute of Technology;Harbin Institute of Technology

  • Venue:
  • ACM Transactions on Asian Language Information Processing (TALIP)
  • Year:
  • 2011

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Abstract

The human-generated question-answer pairs in the Web social communities are of great value for the research of automatic question-answering technique. Due to the large amount of noise information involved in such corpora, it is still a problem to detect the answers even though the questions are exactly located. Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. Since both the questions and their answers usually contain a small number of sentences, the relevance modeling methods have to overcome the problem of word feature sparsity. In this article, the deep learning principle is introduced to address the semantic relevance modeling task. Two deep belief networks with different architectures are proposed by us to model the semantic relevance for the question-answer pairs. According to the investigation of the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, a learning strategy is adopted to promote our models’ performance on the social community corpora without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.