The ACL 2007 Workshop on Statistical Machine Translation (WMT-07), June 23 2007, Prague, Czech Republic
We describe a mixture-model approach to adapting a Statistical Machine Translation system for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation; different methods of assigning weights; and granularity of the source unit being adapted to. The best methods achieve gains of approximately one BLEU percentage point over a state-of-the art non-adapted baseline system.
StatMT'07: Proceedings of the Second Workshop on Statistical Machine Translation: 128–135.