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Conference papers
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.
Cited literature [29 references]
https://hal-normandie-univ.archives-ouvertes.fr/hal-02117658
Contributor : Julien Saunier <>
Submitted on : Thursday, May 2, 2019 - 2:53:53 PM
Last modification on : Friday, December 4, 2020 - 8:22:03 AM
Contributor : Julien Saunier <>
Submitted on : Thursday, May 2, 2019 - 2:53:53 PM
Last modification on : Friday, December 4, 2020 - 8:22:03 AM
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Addressee_Detection___ICAART_2...
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