Learning to translate queries for CLIR
Artem Sokolov, Felix Hieber, Stefan Riezler · 2014
The statistical machine translation (SMT) component of cross-lingual information retrieval (CLIR) systems is often regarded as black box that is optimized for translation quality independent from the retrieval task. In recent work [10], SMT has been tuned for retrieval by training a reranker on $k$-best translations ordered according to their retrieval performance. In this paper we propose a decomposable proxy for retrieval quality that obviates the need for costly intermediate retrieval. Furthermore, we explore the full search space of the SMT decoder by directly optimizing decoder parameters under a retrieval-based objective. Experimental results for patent retrieval show our approach to be a promising alternative to the standard pipeline approach.