Knowledge-aware Pronoun Coreference Resolution
Table 5: Influence of gold mentions. F1 scores on different test sets are reported. Adding human-annotated gold mentions help both the End2end and our model. Best performed model are indicated with the bold font.
['Model', 'Setting', 'CoNLL', 'i2b2']
Providing gold mentions to the End2end model can significantly boost its performance by 6.2 F1 and 2.1 F1 on the CoNLL and i2b2 dataset, respectively. Yet, the performance gain from gold mentions is less for our model. Such results clearly illustrate that our model is able to benefit the mention detection with the help of KG incorporation. Besides that, with the help of gold mentions, our model achieves the comparable (slightly better) performance with the context-and-knowledge model Zhang et al. As we also included one new challenging pronoun type, the demonstrative pronoun, the overall performance of their model is lower than the one reported in the original paper. The reason of our model being better is that more knowledge resources (e.g., OMCS) can be incorporated into our model due to its generalizable design. Moreover, it is more difficult for their method Zhang et al. which is expensive and difficult to acquire.