Probabilistic head-driven parsing for discourse structure

Authors:
Jason Baldridge;Alex Lascarides
Affiliations:
University of Edinburgh, Edinburgh, Scotland, UK;University of Edinburgh, Edinburgh, Scotland, UK
Venue:
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Year:
2005

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Abstract

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 probabilistic head-driven parsing techniques. We show that dialogue-based features regarding turn-taking and domain specific goals have a large positive impact on performance. Our best model achieves an f-score of 43.2% for labelled discourse relations and 67.9% for unlabelled ones, significantly beating a right-branching baseline that uses the most frequent relations.