Statistical Machine Translation for Query Expansion in Answer Retrieval 论文

摘要

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. 1