Syntax-aware Neural Semantic Role Labeling with Supertags
Table 5: Results on the CoNLL 2009 in-domain test set for English. All standard deviations in F1<0.12.
['[BOLD] Non-ensemble System', 'P', 'R', 'F1']
English in-domain. First, we were able to approximately replicate the results from \newcitemarcheggiani2017. Adding dropout and highway connections to our BiLSTM model improves performance by 0.5 points, to 88.1, and adding supertags improves results even further to 88.6. Our supertag model performs even better than the non-ensemble model in \newcitemarcheggiani-titov:2017:EMNLP2017, in which the model is given the complete dependency parse of the sentence. This result suggests that supertags can be even more effective for SRL than a more complete representation of syntax. Furthermore, our supertag-based method with contextual representations achieves 90.2, a new state-of-the-art. Interestingly, the gain from supertagging decreases to 0.2 points (90.2 vs. 90.0) in the presence of contextual representations, suggesting that contextual representations encode some of the same syntactic information that supertags provide.