Paper: Dynamic Topic Adaptation for Phrase-based MT

ACL ID E14-1035
Title Dynamic Topic Adaptation for Phrase-based MT
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014

Translating text from diverse sources poses a challenge to current machine translation systems which are rarely adapted to structure beyond corpus level. We explore topic adaptation on a diverse data set and present a new bilingual vari- ant of Latent Dirichlet Allocation to com- pute topic-adapted, probabilistic phrase translation features. We dynamically in- fer document-specific translation proba- bilities for test sets of unknown origin, thereby capturing the effects of document context on phrase translations. We show gains of up to 1.26 BLEU over the base- line and 1.04 over a domain adaptation benchmark. We further provide an anal- ysis of the domain-specific data and show additive gains of our model in combination with other types of topic-adapted features.