Statistical Machine Translation for Query Expansion in Answer Retrieval
Venue
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07), Prague, Czech Republic (2007)
Publication Year
2007
Authors
Stefan Riezler, Alexander Vasserman, Ioannis Tsochantaridis, Vibhu Mittal, Yi Liu
BibTeX
Abstract
This paper presents a novel approach to query expansion in answer retrieval that
uses Statistical Machine Translation (SMT) techniques to bridge the lexical gap
between questions and answers. SMT-based query expansion is performed on the one
hand by using a SMT-based full-sentence paraphraser to introduce synonyms in the
context the full query, and on the other hand by training an SMT model on
question-answer pairs and expanding queries by answer terms taken from translations
of full queries. We compare these global, context-aware query expansion techniques
with a baseline tfidf model and local query expansion on a database of 10 million
question-answer pairs extracted from FAQ pages. Experimental results show a
significant improvement of SMT-based query expansion over both baselines.
