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 in-
formation 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 sys-
tems.