1. Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction
- Author
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Mukesh Barange, Alexandre Pauchet, Usman Malik, Julien Saunier, Saunier, Julien, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH)
- Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] ,Exploit ,business.industry ,Computer science ,Deep learning ,05 social sciences ,020207 software engineering ,Rule-based system ,Feature selection ,Intelligent Agents ,02 engineering and technology ,computer.software_genre ,Machine learning ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Task (project management) ,Human-Computer Interaction ,Machine Learning ,Intelligent agent ,0202 electrical engineering, electronic engineering, information engineering ,0501 psychology and cognitive sciences ,Artificial intelligence ,Baseline (configuration management) ,business ,computer ,050107 human factors - Abstract
International audience; 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.
- Published
- 2019
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