We explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large generaldomain parallel corpus that are most relevant to the target domain. These sentences may be selected with simple cross-entropy based methods, of which we present three. As these sentences are not themselves identical to the in-domain data, we call them pseudo in-domain subcorpora. These subcorpora -1% the size of the original -can then used to train small domain-adapted Statistical Machine Translation (SMT) systems which outperform systems trained on the entire corpus. Performance is further improved when we use these domain-adapted models in combination with a true in-domain model. The results show that more training data is not always better, and that best results are attained via proper domain-relevant data selection, as well as combining in-and general-domain systems during decoding.
Amittai et al. REF explored domain adaptation by select sentences from a large general domain parallel corpus that are most relevant to the target domain.
Domain Adaptation via Pseudo In-Domain Data Selection
EMNLP
Computer Science