General translation models often still struggle to generate accurate
translations in specialized domains. To guide machine translation practitioners
and characterize the effectiveness of domain adaptation methods under different
data availability scenarios, we conduct an in-depth empirical exploration of
monolingual and parallel data approaches to domain adaptation of pre-trained,
third-party, NMT models in settings where architecture change is impractical.
We compare data centric adaptation methods in isolation and combination. We
study method effectiveness in very low resource (8k parallel examples) and
moderately low resource (46k parallel examples) conditions and propose an
ensemble approach to alleviate reductions in original domain translation
quality. Our work includes three domains: consumer electronic, clinical, and
biomedical and spans four language pairs - Zh-En, Ja-En, Es-En, and Ru-En. We
also make concrete recommendations for achieving high in-domain performance and
release our consumer electronic and medical domain datasets for all languages
and make our code publicly available.

本研究探讨了针对预训练第三方 NMT 模型的单语和平行数据方法在领域适应中的效果，在低资源条件下提出了集成方法来缓解翻译质量的降低，并给出了在三个领域和四个语言对中的建议。