Probabilistic Head-Driven Parsing for Discourse Structure

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.

• Publication year

  2005

• Venue

  Conference on Computational Natural Language Learning

• Publication date

  2005-06-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3115/1706543.1706560
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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