A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
Table 4: Results and comparison with previous works on CoNLL-2009 Chinese test set.
['[BOLD] Methods', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
Results on CoNLL-2009. Our baseline achieves nearly the same performance with Cai et al. Our proposed framework outperforms the best reported result Cai et al. Our experimental result boosts to 88.5 F1 score when the framework is enhanced with BERT representations. However, compared with the results in the settings without BERT, the improvement is fairly small (88.53 - 88.47 = 0.06 F1 score, p>0.1) BERT is employed to boost the performance of our basic SRL model and our proposed framework. To analyze the difference between the two models (Baseline + BERT and Baseline + BERT + Dep (IIR)) , we conduct an analysis on the sentence performance comparison between them, which is inspired by Zhang et al. Based on this finding, how to better integrate syntactic knowledge and BERT representations becomes an interesting and meaningful question, and we leave it for future work.