Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

Zufferey, Sandrine; Popescu-Belis, Andrei; Clark, Alex; Georgescul, Maria; Lalanne, Denis (2005). Shallow Dialogue Processing Using Machine Learning Algorithms (or not). In: Bengio, Samy; Bourlard, Hervé (eds.) Machine Learning for Multimodal Interaction. Lecture Notes in Computer Science: Vol. 3361 (pp. 277-290). Berlin: Springer 10.1007/978-3-540-30568-2_24

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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.

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