  Relation extraction aims to extract relational facts from sentences. Previous
models mainly rely on manually labeled datasets, seed instances or
human-crafted patterns, and distant supervision. However, the human annotation
is expensive, while human-crafted patterns suffer from semantic drift and
distant supervision samples are usually noisy. Domain adaptation methods enable
leveraging labeled data from a different but related domain. However, different
domains usually have various textual relation descriptions and different label
space (the source label space is usually a superset of the target label space).
To solve these problems, we propose a novel model of relation-gated adversarial
learning for relation extraction, which extends the adversarial based domain
adaptation. Experimental results have shown that the proposed approach
outperforms previous domain adaptation methods regarding partial domain
adaptation and can improve the accuracy of distance supervised relation
extraction through fine-tuning.
