Predicate Representations and Polysemy in VerbNet Semantic Parsing James Gung∗ Martha Palmer University of Colorado Boulder University of Colorado Boulder
[email protected] [email protected] Abstract Billy consoled the puppy PB Arg0 console.01 Arg1 Despite recent advances in semantic role la- beling propelled by pre-trained text encoders VN Stimulus amuse-31.1 Experiencer like BERT, performance lags behind when ap- plied to predicates observed infrequently dur- Billy walked the puppy ing training or to sentences in new domains. PB Arg0 walk.01 Arg1 In this work, we investigate how semantic role labeling performance on low-frequency VN Agent run-51.3.2-2-1 Theme predicates and out-of-domain data can be im- Table 1: Comparison of PropBank (PB) and VerbNet proved by using VerbNet, a verb lexicon that (VN) roles for predicates console and walk. VerbNet’s groups verbs into hierarchical classes based on thematic role assignments (e.g. Stimulus vs. Agent shared syntactic and semantic behavior and de- and Experiencer vs. Theme) are more dependent on fines semantic representations describing rela- the predicate than PropBank’s numbered arguments. tions between arguments. We find that Verb- Net classes provide an effective level of ab- straction, improving generalization on low- frequency predicates by allowing them to learn large gains in performance through the use of pre- from the training examples of other predicates trained text encoders like ELMo and BERT (Peters belonging to the same class. We also find et al., 2018; Devlin et al., 2019). Despite these ad- that joint training of VerbNet role labeling and vances, performance on low-frequency predicates predicate disambiguation of VerbNet classes and out-of-domain data remains low relative to for polysemous verbs leads to improvements in-domain performance on higher frequency predi- in both tasks, naturally supporting the extrac- cates.