Paper: Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation

ACL ID E12-1055
Title Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While tech- niques for domain adaptation of monolin- gual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model do- main adaptation and their effect on per- formance, such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple (4?10) data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set.