Contrastive Triple Extraction with Generative Transformer
AbstractTriple 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 attention-masking 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.
How to Cite
Ye, H., Zhang, N., Deng, S., Chen, M., Tan, C., Huang, F., & Chen, H. (2021). Contrastive Triple Extraction with Generative Transformer. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14257-14265. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17677
AAAI Technical Track on Speech and Natural Language Processing III