  Previous domain adaptation research usually neglect the diversity in
translation within a same domain, which is a core problem for adapting a
general neural machine translation (NMT) model into a specific domain in
real-world scenarios. One representative of such challenging scenarios is to
deploy a translation system for a conference with a specific topic, e.g.
computer networks or natural language processing, where there is usually
extremely less resources due to the limited time schedule. To motivate a wide
investigation in such settings, we present a real-world fine-grained domain
adaptation task in machine translation (FDMT). The FDMT dataset (Zh-En)
consists of four sub-domains of information technology: autonomous vehicles, AI
education, real-time networks and smart phone. To be closer to reality, FDMT
does not employ any in-domain bilingual training data. Instead, each sub-domain
is equipped with monolingual data, bilingual dictionary and knowledge base, to
encourage in-depth exploration of these available resources. Corresponding
development set and test set are provided for evaluation purpose. We make
quantitative experiments and deep analyses in this new setting, which
benchmarks the fine-grained domain adaptation task and reveals several
challenging problems that need to be addressed.
