Abstract
This paper presents automatic methods for the classification 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 classification of utterances without in depth processing two methods are presented: Semantic Classification Trees and Polygrams. For both methods the classification 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 classification of dialog acts.
This work was funded by the German Federal Ministry of Education, Science, Research and Technology (BMBF) in the framework of the Verbmobil Project under Grant 01 IV 102 H/0. The responsibility for the contents of this study lies with the authors. The authors wish to thank R. Kuhn for providing the SCT software.
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Mast, M., Niemann, H., Nöth, E., Schukat-Talamazzini, E.G. (1996). Automatic classification of dialog acts with Semantic Classification Trees and Polygrams. In: Wermter, S., Riloff, E., Scheler, G. (eds) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. IJCAI 1995. Lecture Notes in Computer Science, vol 1040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60925-3_49
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DOI: https://doi.org/10.1007/3-540-60925-3_49
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