Shallow Dialogue Processing Using Machine Learning Algorithms (or Not)

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.

• Type: Conference
• Year: 2004
• Where: MLMI
• Authors: Andrei Popescu-Belis, Alexander Clark, Maria Georgescul, Denis Lalanne, Sandrine Zufferey
• Keywords: Dialogue, MLMI 2004, Shallow Dialogue, Shallow Dialogue Analysis