Hybrid Attentive Answer Selection in CQA With Deep Users Modelling


  • Jiahui Wen The University of Queensland
  • Jingwei Ma The University of Queensland
  • Yiliu Feng National University of Defence Technology
  • Mingyang Zhong The University of Queensland




Community Question Answering, Deep Users Modelling, Answer Selection


In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.




How to Cite

Wen, J., Ma, J., Feng, Y., & Zhong, M. (2018). Hybrid Attentive Answer Selection in CQA With Deep Users Modelling. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11840



Main Track: Machine Learning Applications