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Titlebook: 9th International Workshop on Spoken Dialogue System Technology; Luis Fernando D‘Haro,Rafael E. Banchs,Haizhou Li Conference proceedings 2

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發(fā)表于 2025-3-21 19:10:45 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱9th International Workshop on Spoken Dialogue System Technology
影響因子2023Luis Fernando D‘Haro,Rafael E. Banchs,Haizhou Li
視頻videohttp://file.papertrans.cn/102/101166/101166.mp4
發(fā)行地址Presents the outcomes of the ?9th IWSDS, “Towards creating more human-like conversational agent technologies”
學(xué)科分類Lecture Notes in Electrical Engineering
圖書封面Titlebook: 9th International Workshop on Spoken Dialogue System Technology;  Luis Fernando D‘Haro,Rafael E. Banchs,Haizhou Li Conference proceedings 2
影響因子This book presents the outcomes of the 9th International Workshop on Spoken Dialogue Systems (IWSDS), “Towards creating more human-like conversational agent technologies”. It compiles and provides a synopsis of current global research to push forward the state of the art in dialogue technologies, including advances in the context of the classical problems of language understanding, dialogue management and language generation, as well as cognitive topics related to the human nature of conversational phenomena, such as humor, empathy and social context understanding and awareness.
Pindex Conference proceedings 2019
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Advances in Computational Intelligence action.In this paper, we focus on the dialogue act classification in reference interviews to model the behaviors of librarians in the information seeking dialogues.Reference interviews sometimes include rare words and phrases.?Therefore, the existing approaches that use words as units of input ofte
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https://doi.org/10.1007/978-3-662-05609-7ke a human.? A conversational android ERICA is designed to conduct several social roles focused on spoken dialogue, such as attentive listening (similar to counseling) and job interview. Design principles in developing these spoken dialogue systems are described, in particular focused on the attenti
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Fuzzy Modeling to Cope with Ambiguitiess have difficulty in handling new dialog states unseen in model training. This paper proposes a fully data-driven approach to DST that can deal with unseen dialog states.?The approach is based on an RNN with an attention mechanism. The model integrates two variants of RNNs: a decoder that detects an
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https://doi.org/10.1007/978-3-319-97982-3rs.? Speech collisions often occur when a user and the dialog system speak simultaneously.? This study presents a method to generate fillers at the beginning of the system utterances to indicate an intention of turn-taking or turn-holding just like human conversations.? To this end, we analyzed the
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