Adaptation of Statistical Machine Translation Model for Cross-Lingual Information Retrieval in a Service Context

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 109–119, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Adaptation of Statistical Machine Translation Model for Cross-Lingual Information Retrieval in a Service Context Vassilina Nikoulina Xerox Research Center Europe vassilina.nikoulina@xrce.xerox.com Bogomil Kovachev Informatics Institute University of Amsterdam B.K.Kovachev@uva.nl Nikolaos Lagos Xerox Research Center Europe nikolaos.lagos@xrce.xerox.com Christof Monz Informatics Institute University of Amsterdam C.Monz@uva.nl Abstract 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 Introduction Cross Lingual Information Retrieval (CLIR) is an important feature for any digital content provider in today’s multilingual environment. However, many of the content providers are not willing to change existing well-established document index- ing and search tools, nor to provide access to their document collection by a third-party exter- nal service. The work presented in this paper as- sumes such a context of use, where a query trans- lation service allows translating queries posed to the search engine of a content provider into sev- eral target languages, without requiring changes to the undelying IR system used and without ac- cessing, at translation time, the content provider’s document set. Keeping in mind these constraints, we present two approaches on query translation optimisation. One of the important observations done dur- ing the CLEF 2009 campaign (Ferro and Peters, 2009) related to CLIR was that the usage of Sta- tistical Machine Translation (SMT) systems (eg. Google Translate) for query translation led to important improvements in the cross-lingual re- trieval performance (the best CLIR performance increased from ˜55% of the monolingual baseline in 2008 to more than 90% in 2009 for French and German target languages). However, general- purpose SMT systems are not necessarily adapted for query translation. That is because SMT sys- tems trained on a corpus of standard parallel phrases take into account the phrase structure im- plicitly. The structure of queries is very differ- ent from the standard phrase structure: queries are very short and the word order might be different than the typical full phrase one. This problem can be …

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