Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
Table 2: F1 scores on the CoNLL nominal test data.
['Model', 'A0', 'A1', 'A2', 'AM-*', '[ITALIC] All']
Most-frequent and Factorization directly estimate selectional preferences on the verbal domain and apply them to the nominal one, while in our approach we share a selectional preference model to bootstrap our labeler. The lower scores of these baselines suggest that it is indeed crucial to distill this weak signal into the SRL model rather than relying on it at test time. The improvement over Direct-transfer shows that we genuinely benefit from using unlabeled nominal data. This annotation decision is controversial and most other formalism banarescu2013abstract do not use them. Their presence negatively affects our approach as the verbal data has no self-loops.