Contrastive Triple Extraction with Generative Transformer

Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple extraction task for sequence generation. Since generative triple extraction may struggle to capture long-term dependencies and generate unfaithful triples, we introduce a novel model, contrastive triple extraction with a generative transformer. Specifically, we introduce a single shared transformer module for encoder-decoder-based generation. To generate faithful results, we propose a novel triplet contrastive training object. Moreover, we introduce two mechanisms to further improve model performance (i. e. , batch-wise dynamic attentionmasking and triple-wise calibration). Experimental results on three datasets (i. e. , NYT, WebNLG, and MIE) show that our approach achieves better performance than that of baselines.

Authors

• Hongbin Ye
• Ningyu Zhang Zhejiang University Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph
• Shumin Deng NUS-NCS Joint Lab, National University of Singapore
• Mosha Chen
• Chuanqi Tan
• Fei Huang Tongyi Lab
• Huajun Chen College of Computer Science and Technology, Zhejiang University ZJU-Ant Group Joint Lab of Knowledge Graph Zhejiang Key Laboratory of Big Data Intelligent Computing

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