Decision detection using hierarchical graphical models

Authors:
Trung H. Bui;Stanley Peters
Affiliations:
Stanford University, Stanford, CA;Stanford University, Stanford, CA
Venue:
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Year:
2010

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Abstract

We investigate hierarchical graphical models (HGMs) for automatically detecting 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.