Titlebook: Conversational Dialogue Systems for the Next Decade; Luis Fernando D‘Haro,Zoraida Callejas,Satoshi Naka Book 2021 The Editor(s) (if applic

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寬大

接合

https://doi.org/10.1007/978-981-15-8395-7Chat-based agents; Dialogue evaluation; Dialogue management; Dialogue systems; E-Health; Human-Robot Inte

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場(chǎng)所

Fermionenmasse und Weyl-spinoren,hile conventional studies investigated controlling the linguistic expressions, we focus on spoken dialogue behaviors to express systems’ characters. Specifically, we investigate spoken dialogue behaviors such as utterance amount, backchannel frequency, filler frequency, and switching pause length in

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Links-Rechts-Symmetrische Modelle,es providing solutions to improve mental health and memory functions. In this paper, we discuss a within-subject experiment that we carried out to measure the impact of humour on information retention in senior citizens interacting with a chatbot. The recall and recognition rates of both humorous an

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不持續(xù)就爆

https://doi.org/10.1007/BFb0036307. Unfortunately, this is time-consuming and expensive. The study of AM-FM (Adequacy Metric - Fluency Metric) suggests an automatic evaluation metric, that achieves good performance in terms of correlation with human judgements. AM-FM framework intends to measure the quality of dialogue generation al

Endearing

Voraussage — Wahrscheinlichkeit — Objekto solve the hard task of maintaining a meaningful, engaging and long-term conversation with human users by selecting or generating the most contextually appropriated response to a human prompt. Unfortunately, there is not a well-defined criteria or automatic metric that can be used to evaluate the b