Building mutually beneficial relationships between question retrieval and answer ranking to improve performance of community question answering

Man Lan, GuoShun Wu, Chunyun Xiao, Yuanbin Wu, Ju Wu

Abstract: In community-based question answering (CQA) domain, there are two main tasks, i.e., question retrieval and answer ranking. Previous studies addressed these two tasks in an independent manner or in a sequential fashion without information communication. In this work we propose a novel method to improve the performance of CQA by mutually promoting the two tasks with the help of each other. Specifically, we propose two methods to improve question retrieval task by utilizing the rank of answers or extracting novel features from Q-A pairs respectively. Meanwhile, to improve answer ranking, we also present novel features with the help of similar questions. Experimental results on benchmark dataset showed that this mutually beneficial strategy between question retrieval and answer ranking not only improved the individual performance of these two tasks but also improved the overall performance of CQA through reducing errors propagating from question retrieval to answer ranking.