Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood. Unlike previous single-task few-shot problems, relational triple extraction is more challenging as the entities and relations have implicit correlations. In this paper, We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples, namely, entity pairs and corresponding relations. To be specific, we design a hybrid prototypical learning mechanism that bridges text and knowledge concerning both entities and relations. Thus, implicit correlations between entities and relations are injected. Additionally, we propose a prototype-aware regularization to learn more representative prototypes. Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.
@article{arxiv.2010.16059,
title = {Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction},
author = {Haiyang Yu and Ningyu Zhang and Shumin Deng and Hongbin Ye and Wei Zhang and Huajun Chen},
journal= {arXiv preprint arXiv:2010.16059},
year = {2023}
}