Paper: Decision Detection Using Hierarchical Graphical Models

ACL ID P10-2057
Title Decision Detection Using Hierarchical Graphical Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

We investigate hierarchical graphical models (HGMs) for automatically detect- ing decisions in multi-party discussions. Several types of dialogue act (DA) are distinguished on the basis of their roles in formulating decisions. HGMs enable us to model dependencies between observed features of discussions, decision DAs, and subdialogues that result in a decision. For the task of detecting decision regions, an HGM classifier was found to outperform non-hierarchical graphical models and support vector machines, raising the F1-score to 0.80 from 0.55.