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Authors: Usman Malik ; Mukesh Barange ; Julien Saunier and Alexandre Pauchet

Affiliation: Normandie University, INSA Rouen, LITIS – 76000 Rouen and France

ISBN: 978-989-758-350-6

Keyword(s): Human-Computer Interaction, Intelligent Agents, Machine Learning.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Autonomous Systems ; Computational Intelligence ; Conversational Agents ; Enterprise Information Systems ; Evolutionary Computing ; Human-Computer Interaction ; Intelligent User Interfaces ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Robot and Multi-Robot Systems ; Soft Computing ; Symbolic Systems

Abstract: Addressee detection is an important challenge to tackle in order to improve dialogical interactions between humans and agents. This detection, essential for turn-taking models, is a hard task in multiparty conditions. Rule based as well as statistical approaches have been explored. Statistical approaches, particularly deep learning approaches, require a huge amount of data to train. However, smart feature selection can help improve addressee detection on small datasets, particularly if multimodal information is available. In this article, we propose a statistical approach based on smart feature selection that exploits contextual and multimodal information for addressee detection. The results show that our model outperforms an existing baseline.

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Paper citation in several formats:
Malik, U.; Barange, M.; Saunier, J. and Pauchet, A. (2019). Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-350-6, pages 267-274. DOI: 10.5220/0007574602670274

@conference{icaart19,
author={Usman Malik. and Mukesh Barange. and Julien Saunier. and Alexandre Pauchet.},
title={Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2019},
pages={267-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007574602670274},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction
SN - 978-989-758-350-6
AU - Malik, U.
AU - Barange, M.
AU - Saunier, J.
AU - Pauchet, A.
PY - 2019
SP - 267
EP - 274
DO - 10.5220/0007574602670274

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