We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While techniques for domain adaptation of monolingual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model domain adaptation and their effect on performance, 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
