Open Set Relation Extraction via Unknown-Aware Training
Jun Zhao Xin Zhao Wenyu Zhan Qi Zhang Tao Gui Zhongyu Wei Yunwen Chen Xiang Gao Xuanjing Huang
The existing supervised relation extraction methods have achieved impressive
performance in a closed-set setting, where the relations during both training
and testing remain the same. In a more realistic open-set setting, unknown
relations may appear in the test set. Due to the lack of supervision signals
from unknown relations, a well-performing closed-set relation extractor can
still confidently misclassify them into known relations. In this paper, we
propose an unknown-aware training method, regularizing the model by dynamically
synthesizing negative instances. To facilitate a compact decision boundary,
``difficult'' negative instances are necessary. Inspired by text adversarial
attacks, we adaptively apply small but critical perturbations to original
training instances and thus synthesizing negative instances that are more
likely to be mistaken by the model as known relations. Experimental results
show that this method achieves SOTA unknown relation detection without
compromising the classification of known relations.