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

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 653–662, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Phrase-Based Translation Model for Question Retrieval in Community Question Answer Archives Guangyou Zhou, Li Cai, Jun Zhao∗, and Kang Liu National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences 95 Zhongguancun East Road, Beijing 100190, China {gyzhou,lcai,jzhao,kliu}@nlpr.ia.ac.cn 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. 1 Introduction Over the past few years, large scale question and answer (Q&A) archives have become an important information resource on the Web. These include the traditional Frequently Asked Questions (FAQ) archives and the emerging community-based Q&A services, such as Yahoo! Answers1, Live QnA2, and Baidu Zhidao3. ∗Correspondence author: jzhao@nlpr.ia.ac.cn 1http://answers.yahoo.com/ 2http://qna.live.com/ 3http://zhidao.baidu.com/ Community-based Q&A services can directly re- turn answers to the queried questions instead of a list of relevant documents, thus provide an effective alternative to the traditional adhoc information re- trieval. To make full use of the large scale archives of question-answer pairs, it is critical to have func- tionality helping users to retrieve historical answers (Duan et al., 2008). Therefore, it is a meaningful task to retrieve the questions that are semantically equivalent or relevant to the queried questions. For example in Table 1, given question Q1, Q2 can be re- turned and their answers will then be used to answer Q1 because the answer of Q2 is expected to partially satisfy the queried question Q1. This is what we called question retrieval in this paper. The major challenge for Q&A retrieval, as for Query: Q1: How to get rid of stuffy nose? Expected: Q2: What is the best way to prevent a cold? Not Expected: Q3: How do I air out my stuffy room? Q4: How do you make a nose bleed stop quicker? Table 1: An example on question retrieval most information retrieval models, such as vector space model (VSM) (Salton et al., 1975), Okapi model (Robertson et al., 1994), language model (LM) (Ponte and Croft, 1998), is the lexical gap (or lexical chasm) between the queried questions and the historical questions in the archives (Jeon et al., 2005; Xue et al., 2008). For example in Table 1, Q1 and Q2 are two semantically similar questions, but they have very few words in …

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