Syntax-aware Multilingual Semantic Role Labeling
Table 4: Results of full end-to-end model. PD denotes the accuracy of predicate disambiguation. + AP represents biaffine SRL+Argument Pruning Layer, while the last row indicates biaffine SRL+Argument Pruning Layer+BERT.
['Model', 'Catalan PD', 'Catalan F1', 'Chinese PD', 'Chinese F1', 'English PD', 'English F1', 'German PD', 'German F1', 'Spanish PD', 'Spanish F1']
Compared to the baseline, our full end-to-end model (Biaffine SRL) yields slightly higher precision of predicate disambiguation as a whole, which gives rise to a corresponding gain of semantic F1. What is more, our model (using argument pruning and BERT) reaches the highest scores on the five benchmarks. Besides, experiments indicate that argument pruning promotes role labeling performance while BERT significantly improves the performance of predicate disambiguation.