2020

Contrastive Triple Extraction with Generative Transformer

Ye, Hongbin, Zhang, Ningyu, Deng, Shumin et al.

Understand

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.

Built on

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