Authors:
Usman Malik
;
Mukesh Barange
;
Julien Saunier
and
Alexandre Pauchet
Affiliation:
Normandie University, INSA Rouen, LITIS – 76000 Rouen and France
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.