Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction

• URL: http://arxiv.org/abs/2010.16059v1
• Date: Fri, 30 Oct 2020 04:18:39 GMT
• Title: Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
• Authors: Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye, Wei Zhang, Huajun Chen
• Abstract summary: We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples. We design a hybrid learning mechanism that bridges text and knowledge concerning both entities and relations. Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.
• Score: 40.00702385889112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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