Can Semantic Roles Generalize Across Genres?

PropBank has been widely used as train- ing data for Semantic Role Labeling. However, because this training data is taken from the WSJ, the resulting machine learning models tend to overfit on idiosyn- crasies of that text's style, and do not port well to other genres. In addition, since PropBank was designed on a verb-by-verb basis, the argument labels Arg2 - Arg5 get used for very diverse argument roles with inconsistent training instances. For exam- ple, the verb "make" uses Arg2 for the "Material" argument; but the verb "multi- ply" uses Arg2 for the "Extent" argument. As a result, it can be difficult for auto- matic classifiers to learn to distinguish ar- guments Arg2-Arg5. We have created a mapping between PropBank and VerbNet that provides a VerbNet thematic role la- bel for each verb-specific PropBank label. Since VerbNet uses argument labels that are more consistent across verbs, we are able to demonstrate that these new labels are easier to learn.