Agreement Detection in Multiparty Conversation

Sebastian Germesin, Theresa Wilson

In: James Crowley , Yuri Ivanov , Christopher Wren (Hrsg.). ICMI-MLMI '09: Proceedings of the 2009 International Conference on Multimodal Interfaces. International Conference on Multimodal Interfaces (ICMI) Machine Learning and Multimodal Interaction November 2-6 Cambridge MA United States Seiten 7-13 ISBN 978-1-60558-772-1 ACM 11/2009.


This paper presents a system for the automatic detection of agreements in multi-party conversations. We investigate various types of features that are useful for identifying agreements, including lexical, prosodic, and structural features. This system is implemented using supervised machine learning techniques and yields competitive results: Accuracy of 98.1% and a kappa value of 0.4. We also begin to explore the novel task of detecting the addressee of agreements (which speaker is being agreed with). Our system for this task achieves an accuracy of 80.3%, a 56% improvement over the baseline.

germesin_agree_icmi09-7.pdf (pdf, 316 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence