Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1731 - 1738 December 7-11, 2022 ©2022 Association for Computational Linguistics Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction Zhen Wan ∗1 Qianying Liu ∗1 Zhuoyuan Mao1 Fei Cheng1 Sadao Kurohashi1 Jiwei Li2 1 Kyoto University, Japan 2 Zhejiang University, China {zhenwan, ying, zhuoyuanmao}@nlp.ist.i.kyoto-u.ac.jp {feicheng, kuro}@i.kyoto-u.ac.jp {jiwei_li}@zju.edu.cn Abstract Relation extraction (RE) has achieved remark- able progress with the help of pre-trained lan- guage models. However, existing RE models are usually incapable of handling two situa- tions: implicit expressions and long-tail rela- tion types, caused by language complexity and data sparsity. In this paper, we introduce a sim- ple enhancement of RE using k nearest neigh- bors (kNN-RE). kNN-RE allows the model to consult training relations at test time through a nearest-neighbor search and provides a simple yet effective means to tackle the two issues above. Additionally, we observe that kNN- RE serves as an effective way to leverage dis- tant supervision (DS) data for RE. Experimen- tal results show that the proposed kNN-RE achieves state-of-the-art performances on a va- riety of supervised RE datasets, i.e., ACE05, SciERC, and Wiki80, along with outperform- ing the best model to date on the i2b2 and Wiki80 datasets in the setting of allowing us- ing DS. Our code and models are available at: https://github.com/YukinoWan/kNN-RE. 1 Introduction Relation extraction (RE) aims to identify the rela- tionship between entities mentioned in a sentence, and is beneficial to a variety of downstream tasks such as question answering and knowledge base population. Recent studies (Zhang et al., 2020; Zeng et al., 2020; Lin et al., 2020; Wang and Lu, 2020; Cheng et al., 2020; Zhong and Chen, 2021) in supervised RE take advantage of pre-trained lan- guage models (PLMs) and achieve SOTA perfor- mances by fine-tuning PLMs with a relation classi- fier. However, we observe that existing RE models are usually incapable of handling two RE-specific situations : implicit expressions and long-tail re- lation types. Implicit expression refers to the situation where a relation is expressed as the underlying message ∗ This denotes equal contribution. Nearest Neighbor: He was the younger bro- ther of Panagiotis and Athanasios Sekeris. Test example: He is the youngest son of Liones, comparing with Samuel Liones and Henry Liones. Gold label: sibling to Implicit Expression Long-tail Relation Types Model prediction: spouse Gold label: sibling to child of spouse sibling to title country of birth employee of Test example Decision boundary Figure 1: Left: the retrieved example has a similar struc- ture but with the phrase “younger brother”, it becomes easier to infer. Right: Referring to the gold labels of nearest neighbors can reduce the bias. Highlighted words may directly influence on the relation prediction. that is not explicitly stated or shown. For exam- ple, for the relation “sibling to”, a common expres- sion can be “He has a brother James”, while an implicit expression could be “He is the youngest son of Liones, comparing with Samuel Liones and Henry …

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