Shallow dialogue processing using machine learning algorithms (or not)

Authors:
Andrei Popescu-Belis;Alexander Clark;Maria Georgescul;Denis Lalanne;Sandrine Zufferey
Affiliations:
School of Translation and Interpreting (ETI), TIM/ISSCO, University of Geneva, Geneva 4, Switzerland;School of Translation and Interpreting (ETI), TIM/ISSCO, University of Geneva, Geneva 4, Switzerland;School of Translation and Interpreting (ETI), TIM/ISSCO, University of Geneva, Geneva 4, Switzerland;Faculty of Science, DIUF/DIVA, University of Fribourg, Fribourg, Switzerland;School of Translation and Interpreting (ETI), TIM/ISSCO, University of Geneva, Geneva 4, Switzerland
Venue:
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Year:
2004

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Abstract

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.