Syntax-aware Neural Semantic Role Labeling with Supertags
Table 8: English in-domain test results by predicate category and role label. The mate-tools Björkelund et al. (2009) and Path-LSTM results are taken from roth2016.
['Model', 'V / A0 P', 'V / A0 R', 'V / A0 F', 'V / A1 P', 'V / A1 R', 'V / A1 F', 'V / A2 P', 'V / A2 R', 'V / A2 F', 'V / AM P', 'V / AM R', 'V / AM F']
(V: Verb, Propbank; N: Noun, Nombank) and semantic role. We can observe that the various supertag models differ in their performance for different predicate-role pairs, suggesting that different kinds of linguistic information are relevant for identifying the different roles. Overall, Model 1 supertags achieve the most consistent improvements over BiLSTM and BiLSTM + Dropout + Highway (BDH) in V / A0, V / A1, V / A2, V / AM, N / A2, and N / AM. Moreover, Model 1 even improves on Path-LSTM Roth and Lapata This shows that supertags can be even more effective for SRL than more granular syntactic information–even quite simple supertags, like Model 0, which encode only the dependency arc between a word and its head.