Adaptation of Statistical Machine Translation Model for Cross-Lingual Information Retrieval in a Service Context
Vassilina Nikoulina, Bogomil Kovachev, Nikolaos Lagos, Christof Monz · UvA-DARE (University of Amsterdam) · 2012
This work proposes to adapt an existing general SMT model for the task of translat-ing queries that are subsequently going to be used to retrieve information from a tar-get language collection. In the scenario that we focus on access to the document collec-tion itself is not available and changes to the IR model are not possible. We propose two ways to achieve the adaptation effect and both of them are aimed at tuning pa-rameter weights on a set of parallel queries. The first approach is via a standard tuning procedure optimizing for BLEU score and the second one is via a reranking approach optimizing for MAP score. We also extend the second approach by using syntax-based features. Our experiments show improve-ments of 1-2.5 in terms of MAP score over the retrieval with the non-adapted transla-tion. We show that these improvements are due both to the integration of the adapta-tion and syntax-features for the query trans-lation task. 1