A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Table 3: F1-score of extracting relational triples from sentences with different number (denoted as N) of triples.
['Method', 'NYT  [ITALIC] N=1', 'NYT  [ITALIC] N=2', 'NYT  [ITALIC] N=3', 'NYT  [ITALIC] N=4', 'NYT  [ITALIC] N≥5', 'WebNLG  [ITALIC] N=1', 'WebNLG  [ITALIC] N=2', 'WebNLG  [ITALIC] N=3', 'WebNLG  [ITALIC] N=4', 'WebNLG  [ITALIC] N≥5']
It can be seen that the performance of most baselines on Normal, EPO and SEO presents a decreasing trend, reflecting the increasing difficulty of extracting relational triples from sentences with different overlapping patterns. That is, among the three overlapping patterns, Normal class is the easiest pattern while EPO and SEO classes are the relatively harder ones for baseline models to extract. In contrast, the proposed CasRel model attains consistently strong performance over all three overlapping patterns, especially for those hard patterns. We also validate the CasRel’s capability in extracting relational triples from sentences with different number of triples. Again, the CasRel model achieves excellent performance over all five classes. Though it’s not surprising to find that the performance of most baselines decreases with the increasing number of relational triples that a sentence contains, some patterns still can be observed from the performance changes of different models. Compared to previous works that devote to solving the overlapping problem in relational triple extraction, our model suffers the least from the increasing complexity of the input sentence. Though the CasRel model gain considerable improvements on all five classes compared to the best state-of-the-art method CopyRRL zeng2019Learning, the greatest improvement of F1-score on the two datasets both come from the most difficult class (N≥5), indicating that our model is more suitable for complicated scenarios than the baselines.