Answer Selection in Community Question Answering by Normalizing Support Answers

Abstract

Answer selection in community question answering (cQA) is a common task in natural language processing. Recent progress focuses on not only pure question-answer (QA) match but also support answers [4]. In this paper, we argue that the performance can drop dramatically if noisy support answers are selected. To tackle the above issue, we propose a novel way to leverage the contributions of support answers: the match scores which are firstly normalized by the correlations between the question and the corresponding similar questions, such that the negative effect from the noisy answers can be reduced. The model applies word-to-word attention to improve QA match and employs cosine similarity as the normalization factor for support answers. Compared with previous work, experiments on the Yahoo! Answers L4 dataset show that our model achieves superior P@1 and MRR results.