Paper: Discourse Generation Using Utility-Trained Coherence Models

ACL ID P06-2103
Title Discourse Generation Using Utility-Trained Coherence Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

We describe a generic framework for inte- grating various stochastic models of dis- course coherence in a manner that takes advantage of their individual strengths. An integral part of this framework are algo- rithms for searching and training these stochastic coherence models. We evaluate the performance of our models and algo- rithms and show empirically that utility- trained log-linear coherence models out- perform each of the individual coherence models considered.