Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction
Van-Hien Tran, Hiroki Ouchi, Hiroyuki Shindo, Yūji Matsumoto, Taro Watanabe · Journal of Natural Language Processing · 2023
Zero-shot relation extraction aims to recognize (new) unseen relations that cannot be observed during training.Due to this point, recognizing unseen relations with no corresponding labeled training instances is a challenging task.Recognizing an unseen relation between two entities in an input instance at the testing time, a model needs to grasp the semantic relationship between the instance and all unseen relations to make a prediction.This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task.A new model entirely devoted to this goal through three main aspects was proposed: learning effective relation representation, designing purposeful mini-batches, and binding two-way semantic consistency.Experimental results on two benchmark datasets demonstrate that our approach significantly improves task performance and achieves state-of-the-art results.Our source code and data are publicly available.