Adaptation of Statistical Machine Translation Models for Cross-Lingual Information Retrieval in a Service Context
Vassilina Nikoulina, Bogomil Koltchev Kovatchev, Christof Monz, Nikolaos Lagos
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.
EACL - 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon, France, April 23-27, 2012.