A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Table 2: Results of different methods on NYT and WebNLG datasets. Our re-implementation is marked by *.
['Method', 'NYT  [ITALIC] Prec.', 'NYT  [ITALIC] Rec.', 'NYT  [ITALIC] F1', 'WebNLG  [ITALIC] Prec.', 'WebNLG  [ITALIC] Rec.', 'WebNLG  [ITALIC] F1']
The CasRel model overwhelmingly outperforms all the baselines in terms of all three evaluation metrics and achieves encouraging 17.5% and 30.2% improvements in F1-score over the best state-of-the-art method zeng2019Learning on NYT and WebNLG datasets respectively. Even without taking advantage of the pre-trained BERT, CasRelrandom and CasRelLSTM are still competitive to existing state-of-the-art models. This validates the utility of the proposed cascade decoder that adopts a novel binary tagging scheme. The performance improvements from CasRelrandom to CasRel highlight the importance of the prior knowledge in a pre-trained language model.