Modeling Conversational Systems with a Spoken Dialogue Model


Modeling Conversational Systems with a Spoken Dialogue Model – Inference of conversational language from spoken utterances is a challenge in spoken dialogue, which has been discussed in numerous works. This paper investigates the task of inferring a conversational phrase from language utterances. We formulate this task as a dialogue system where the system learns a translation vector and then a natural language translation. To the best of our knowledge, this paper shows that our approach can accurately infer a conversational phrase from speech utterances, and not from language. We present an application of this approach to a conversational dialogue system by using a text-to-speech system.

The goal of this paper is to provide an efficient and robust implementation of a new distributed inference methodology that is able to capture and model the dependencies among agents. We describe the algorithm and the implementation for a new policy architecture, which supports many agents, including many robots. We also discuss the possibility of a future vision for our methodology, which is based on learning to reason.

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Modeling Conversational Systems with a Spoken Dialogue Model

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    Convergent Inference Policies for Reinforcement LearningThe goal of this paper is to provide an efficient and robust implementation of a new distributed inference methodology that is able to capture and model the dependencies among agents. We describe the algorithm and the implementation for a new policy architecture, which supports many agents, including many robots. We also discuss the possibility of a future vision for our methodology, which is based on learning to reason.


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