Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification

Relation classification (RC) is an important task in knowledge extraction from texts, while data-driven approaches, although achieving high performance, heavily rely on a large amount of annotated training data. Recently, many few-shot RC models have been proposed and yielded promising results in general domain datasets, but when adapting to a specific domain, such as medicine, the performance drops dramatically. In this paper, we propose a Knowledge-Enhanced Few-shot RC model for the Domain Adaptation task (KEFDA), which incorporates general and domain-specific knowledge graphs (KGs) to the RC model to improve its domain adaptability. With the help of concept-level KGs, the model can better understand the semantics of texts and easily summarize the global semantics of relation types from only a few instances. To be more important, as a kind of meta-information, the manner of utilizing KGs can be transferred from existing tasks to new tasks, even across domains. Specifically, we design a knowledge-enhanced prototypical network to conduct instance matching, and a relation-meta learning network for implicit relation matching. The two scoring functions are combined to infer the relation type of a new instance. Experimental results on the Domain Adaptation Challenge in the FewRel 2.0 benchmark demonstrate that our approach significantly outperforms the state-of-the-art models (by 6.63% on average).