Paper: Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization

ACL ID P13-1084
Title Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Community question answering (CQA) has become an increasingly popular re- search topic. In this paper, we focus on the problem of question retrieval. Question retrieval in CQA can automatically find the most relevant and recent questions that have been solved by other users. However, the word ambiguity and word mismatch problems bring about new challenges for question retrieval in CQA. State-of-the-art approaches address these issues by implic- itly expanding the queried questions with additional words or phrases using mono- lingual translation models. While use- ful, the effectiveness of these models is highly dependent on the availability of quality parallel monolingual corpora (e.g., question-answer pairs) in the absence of which they are troubled by noise issue. In this work, we propos...