Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges
  and Opportunities
Riccardo Orlando Simone Conia Roberto Navigli
Although we have witnessed impressive progress in Semantic Role Labeling
(SRL), most of the research in the area is carried out assuming that the
majority of predicates are verbs. Conversely, predicates can also be expressed
using other parts of speech, e.g., nouns and adjectives. However, non-verbal
predicates appear in the benchmarks we commonly use to measure progress in SRL
less frequently than in some real-world settings -- newspaper headlines,
dialogues, and tweets, among others. In this paper, we put forward a new
PropBank dataset which boasts wide coverage of multiple predicate types. Thanks
to it, we demonstrate empirically that standard benchmarks do not provide an
accurate picture of the current situation in SRL and that state-of-the-art
systems are still incapable of transferring knowledge across different
predicate types. Having observed these issues, we also present a novel,
manually-annotated challenge set designed to give equal importance to verbal,
nominal, and adjectival predicate-argument structures. We use such dataset to
investigate whether we can leverage different linguistic resources to promote
knowledge transfer. In conclusion, we claim that SRL is far from "solved", and
its integration with other semantic tasks might enable significant improvements
in the future, especially for the long tail of non-verbal predicates, thereby
facilitating further research on SRL for non-verbal predicates.