A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
Table 2: Results and comparison with previous works on CPB1.0 test set.
['[BOLD] Methods', '[BOLD] F1']
It is clear that adding dependency trees into the baseline SRL model can effectively improve the performance (p<0.0001), no matter whether employ the BERT representations or not. Especially, our proposed framework (IIR) consistently outperforms the hard parameter sharing strategy. So we only report the results of our proposed framework in later experiments. Our final results outperforms the best previous model Xia et al. Compared with the large amount of works on English SRL, Chinese SRL works are rare, mainly because of the limitation of datasize and lack of attention of Chinese researchers. Sun et al. Wang et al. Xia et al. Different from them, we propose a MTL framework to integrate implicit syntactic representations into a simple unified model on both span-based and word-based SRL, achieving substantial improvements.