Paper: Probabilistic Head-Driven Parsing For Discourse Structure

ACL ID W05-0613
Title Probabilistic Head-Driven Parsing For Discourse Structure
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

We describe a data-driven approach to building interpretable discourse structures for appointment scheduling dialogues. We represent discourse structures as headed trees and model them with probabilis- tic head-driven parsing techniques. We show that dialogue-based features regard- ing turn-taking and domain speci c goals have a large positive impact on perfor- mance. Our best model achieves an f- score of 43.2% for labelled discourse rela- tions and 67.9% for unlabelled ones, sig- ni cantly beating a right-branching base- line that uses the most frequent relations.