Phrase-Based Translation Model for Question Retrieval in Community Question Answer Archives

Guangyou Zhou,  Li Cai,  Jun Zhao,  Kang Liu
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences


Abstract

Community-based question answer (Q&A) has become an important issue due to the pop- ularity of Q&A archives on the web. This pa- per is concerned with the problem of ques- tion retrieval. Question retrieval in Q&A archives aims to find historical questions that are semantically equivalent or relevant to the queried questions. In this paper, we propose a novel phrase-based translation model for question retrieval. Compared to the traditional word-based translation models, the phrase- based translation model is more effective be- cause it captures contextual information in modeling the translation of phrases as a whole, rather than translating single words in isola- tion. Experiments conducted on real Q&A data demonstrate that our proposed phrase- based translation model significantly outper- forms the state-of-the-art word-based transla- tion model.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-1066.pdf