Adaptation of Statistical Machine Translation Models for Cross-Lingual Information Retrieval in a Service Context - Naver Labs Europe
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This work proposes to adapt an existing general SMT model for the task of translating queries that are subsequently going tobe used to retrieve information from a target language collection. In the scenario that we focus on access to the document collection 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 parameter weights on a set of parallel queries. The flrst 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 showed improvements of 1-2.5 in terms of MAP score over the retrieval with the nonadapted translation. We have shown that these improvements are due both to the integration of the adaptation and syntax features for the query translation task.