Automatic classification of dialog acts with semantic classification trees and polygrams

This paper presents automatic methods for the classiication of dialog acts. In the verbmobil application (speech-to-speech translation of face-to-face dialogs) maximally 50 % of the utterances are analyzed in depth and for the rest, shallow processing takes place. The dialog component keeps track of the dialog with this shallow processing. For the classiication of utterances without in depth processing two methods are presented: Semantic Classiication Trees and Polygrams. For both methods the classiication algorithm is trained automatically from a corpus of labeled data. The novel idea with respect to SCTs is the use of dialog state dependent CTs and with respect to Polygrams it is the use of competing language models for the classiication of dialog acts.