  Previous research for adapting a general neural machine translation (NMT)
model into a specific domain usually neglects the diversity in translation
within the same domain, which is a core problem for domain adaptation 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.,
global warming or coronavirus, where there are usually extremely less resources
due to the limited schedule. To motivate wider investigation in such a
scenario, we present a real-world fine-grained domain adaptation task in
machine translation (FGraDA). The FGraDA dataset consists of Chinese-English
translation task for four sub-domains of information technology: autonomous
vehicles, AI education, real-time networks, and smart phone. Each sub-domain is
equipped with a development set and test set for evaluation purposes. To be
closer to reality, FGraDA does not employ any in-domain bilingual training data
but provides bilingual dictionaries and wiki knowledge base, which can be
easier obtained within a short time. We benchmark the fine-grained domain
adaptation task and present in-depth analyses showing that there are still
challenging problems to further improve the performance with heterogeneous
resources.
