Domain Adaptation via Pseudo In-Domain Data Selection

TLDR

The study investigates efficient domain adaptation for statistical machine translation by selecting target‑domain relevant sentences from a large general‑domain parallel corpus. The authors select pseudo in‑domain subcorpora using three cross‑entropy based methods from the general corpus. The resulting 1 % pseudo in‑domain subcorpora enable small domain‑adapted SMT systems that outperform models trained on the full corpus, and combining them with a true in‑domain model further improves performance, demonstrating that selective data and hybrid decoding yield the best results.

Abstract

We explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large general-domain 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.