RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot
  Relation Extraction
Jun Zhao Wenyu Zhan Xin Zhao Qi Zhang Tao Gui Zhongyu Wei Junzhe Wang Minlong Peng Mingming Sun
Semantic matching is a mainstream paradigm of zero-shot relation extraction,
which matches a given input with a corresponding label description. The
entities in the input should exactly match their hypernyms in the description,
while the irrelevant contexts should be ignored when matching. However, general
matching methods lack explicit modeling of the above matching pattern. In this
work, we propose a fine-grained semantic matching method tailored for zero-shot
relation extraction. Following the above matching pattern, we decompose the
sentence-level similarity score into entity and context matching scores. Due to
the lack of explicit annotations of the redundant components, we design a
feature distillation module to adaptively identify the relation-irrelevant
features and reduce their negative impact on context matching. Experimental
results show that our method achieves higher matching $F_1$ score and has an
inference speed 10 times faster, when compared with the state-of-the-art
methods.