Discourse generation using utility-trained coherence models.pdfVIP

Discourse Generation Using Utility-Trained Coherence Models Radu Soricut Information Sciences Institute University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 radu@ Daniel Marcu Information Sciences Institute University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 marcu@ Abstract 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. 1 Introduction Various theories of discourse coherence (Mann and Thompson, 1988; Grosz et al., 1995) have been applied successfully in discourse analy- sis (Marcu, 2000; Forbes et al., 2001) and dis- course generation (Scott and de Souza, 1990; Kib- ble and Power, 2004). Most of these efforts, how- ever, have limited applicability. Those that use manually written rules model only the most visi- ble discourse constraints (e.g., the discourse con- nective “although” marks a CONCESSION relation), while being oblivious to fine-grained lexical indi- cators. And the methods that utilize manually an- notated corpora (Carlson et al., 2003; Karamanis et al., 2004) and supervised learning algorithms have high costs associated with the annotation pro- cedure, and cannot be easily adapted to different domains and genres. In contrast, more recent research has focused on stochastic approaches that model discourse coher- ence at the local lexical (Lapata, 2003) and global levels (Barzilay and Lee, 2004), while preserving regularities recognized by classic discourse theo- ries (Barzilay and Lapata, 2005). These stochas- tic coherence models use simple, non-hierarchical repr