Multi-perspective respondent representations for answer ranking in community question answering

Abstract

Answer ranking is an important task in community question answering (CQA) systems. It aims at ranking useful answers above useless answers. Existing works learn respondents’ expertise to help estimate qualities of answers. However, in most of these works, the expertise is only learned from the history answers. As a result, structure correlations between question raisers and respondents are usually ignored. Besides, these works lack an efficient way to learn respondent expertise from extensive history answers. To address the limitations, we propose a novel multi-perspective respondent representation learning(MPRR) network. First, our model learns embeddings of raisers and respondents through a heterogeneous information network(HIN) constructed by the answering records in CQA websites. The structure correlations between raisers and respondents are preserved in the learned embeddings. Second, a freezed pre-trained language model is used to learn respondents’ expertise from history answer contents more quickly. Then the multi-perspective respondent representations are generated based on their expertise and the embeddings learned in the HIN. At last, the raisers, respondents, questions, and answers are all considered to compute the matching scores. We evaluate our model on three real-world CQA dataset. Experiment results show that MPRR outperforms all baseline models with three ranking metrics on all datasets.