Role Semantics for Better Models of Implicit Discourse Relations
This work addresses the problem of improving discourse parsing for NLP researchers, but it is incremental as it builds on existing methods with new features.
The paper tackled the challenge of classifying implicit discourse relations by introducing semantic role features, achieving competitive results with other feature-rich approaches on the PDTB.
Predicting the structure of a discourse is challenging because relations between discourse segments are often implicit and thus hard to distinguish computationally. I extend previous work to classify implicit discourse relations by introducing a novel set of features on the level of semantic roles. My results demonstrate that such features are helpful, yielding results competitive with other feature-rich approaches on the PDTB. My main contribution is an analysis of improvements that can be traced back to role-based features, providing insights into why and when role semantics is helpful.