Discourse generation using utility-trained coherence models

  • Authors:
  • Radu Soricut;Daniel Marcu

  • Affiliations:
  • University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA

  • Venue:
  • COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
  • Year:
  • 2006

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Abstract

We describe a generic framework for integrating various stochastic models of discourse coherence in a manner that takes advantage of their individual strengths. An integral part of this framework are algorithms for searching and training these stochastic coherence models. We evaluate the performance of our models and algorithms and show empirically that utility-trained log-linear coherence models outperform each of the individual coherence models considered.