Probabilistic Head-Driven Parsing for Discourse Structure

Jason Baldridge, Alex Lascarides

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)
Place of PublicationAnn Arbor, Michigan
PublisherAssociation for Computational Linguistics
Number of pages8
Publication statusPublished - 1 Jun 2005


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