Temporal Interaction and Causal Influence in Community-Based Question Answering

• Published in: IEEE transactions on knowledge and data engineering Vol. 29; no. 10; pp. 2304 - 2317
• Main Authors: Wu, Fei, Duan, Xinyu, Xiao, Jun, Zhao, Zhou, Tang, Siliang, Zhang, Yin, Zhuang, Yueting
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; causal influence; Communities; Community-based question answering; Computer architecture; Knowledge discovery; long short-term memory; Microprocessors; Recurrent neural networks; Semantics; State of the art; temporal interaction
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2017.2720737

During the last decade, community-based question answering (CQA) sites have accumulated a vast amount of questions and their crowdsourced answers over time. How to efficiently identify the quality of answers that are relevant to a given question has become an active line of research in CQA. The major challenge of CQA is the accurate selection of high-quality answers w.r.t given questions. Previous approaches tend to model the semantic matching between individual pair of one question and its corresponding answer (how fitting an answer is to a posted question). However, these works ignore the temporal interactions between answers (how previous answers influence the late posted answers). For example, a rational user likely adapts others' opinions, revises his inclinations, and posts a more appropriate answer after understanding the given question and previously posted answers. As a result, this paper devises an architecture named Temporal Interaction and Causal Influence LSTM (TC-LSTM) to effectively leverage not only the causal influence between question-answer (how appropriate an answer is for a given question) but also the temporal interactions between answers-answer (how a high-quality answer gradually forms). In particular, long short-term memory (LSTM) is used to capture the explicit question-answer influence and the implicit answers-answer interactions. Experiments are conducted on SemEval 2015 CQA dataset for answer classification task and Baidu Zhidao Dataset for answer ranking task. The experimental results show the advantage of our model comparing with other state-of-the-art methods.