- 著者
-
Van-Hien Tran
Hiroki Ouchi
Hiroyuki Shindo
Yuji Matsumoto
Taro Watanabe
- 出版者
- The Association for Natural Language Processing
- 雑誌
- 自然言語処理 (ISSN:13407619)
- 巻号頁・発行日
- vol.30, no.2, pp.304-329, 2023 (Released:2023-06-15)
- 参考文献数
- 52
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.