Paper: Statistical Machine Translation for Query Expansion in Answer Retrieval

ACL ID P07-1059
Title Statistical Machine Translation for Query Expansion in Answer Retrieval
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

We present an approach to query expan- sion in answer retrieval that uses Statisti- cal Machine Translation (SMT) techniques to bridge the lexical gap between ques- tions and answers. SMT-based query ex- pansion is done by i) using a full-sentence paraphraser to introduce synonyms in con- text of the entire query, and ii) by trans- lating query terms into answer terms us- ing a full-sentence SMT model trained on question-answer pairs. We evaluate these global, context-aware query expansion tech- niques on tfidf retrieval from 10 million question-answer pairs extracted from FAQ pages. Experimental results show that SMT- based expansion improves retrieval perfor- mance over local expansion and over re- trieval without expansion.