Adaptation of the Translation Model for Statistical Machine Translation based on Information Retrieval

In this paper we present experiments concerning translation model adaptation for statistical machine translation. We develop a method to adapt translation models using information retrieval. The approach selects sentences similar to the test set to form an adapted training corpus. The method allows a better use of additionally available out-of-domain training data or finds in-domain data in a mixed corpus. The adapted translation models significantly improve the translation performance compared to competitive baseline systems.