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

作者：Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye, Wei Zhang, Huajun Chen · 年份：2020 · DOI：10.18653/v1/2020.coling-main.563 · 被引用次数：48 · 研究领域：Topic Modeling、Domain Adaptation and Few-Shot Learning、Text and Document Classification Technologies

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.