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Communication dans un congrès
Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction
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.
Littérature citée [29 références]
https://hal-normandie-univ.archives-ouvertes.fr/hal-02117658
Contributeur : Julien Saunier <>
Soumis le : jeudi 2 mai 2019 - 14:53:53
Dernière modification le : dimanche 9 février 2020 - 20:55:12
Contributeur : Julien Saunier <>
Soumis le : jeudi 2 mai 2019 - 14:53:53
Dernière modification le : dimanche 9 février 2020 - 20:55:12
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