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Showing 1 to 11 of 11 for “"Spoken Dialogue Systems"”.
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Discriminative methods for statistical spoken dialogue systems
Dialogue promises a natural and effective method for users to interact with and obtain information from computer systems. Statistical spoken dialogue systems are able to disambiguate in the presence of errors by maintaining probability distributions over what they believe to be the state of a …
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Data-Driven Language Understanding for Spoken Dialogue Systems
Spoken dialogue systems provide a natural conversational interface to computer applications. In recent years, the substantial improvements in the performance of speech recognition engines have helped shift the research focus to the next component of the dialogue system pipeline: the one in charge …
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Sample-Efficient Reinforcement Learning for Spoken Dialogue Systems
… the development of a general open-domain dialogue system capable of engaging in natural conversations with humans remains a challenging task. Developing effective dialogue systems poses a challenge in the face of non-deterministic environments. Users exhibit diverse behaviors, making it …
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Deep learning for spoken dialogue systems : application to nutrition
… models, we collected 31,712 written and 2,962 spoken meal descriptions that were weakly annotated with only information about which database foods were described in the meal, but not explicitly where they were mentioned. Our best deep learning models achieve 95.8% average semantic tagging F1 …
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Developing attribute acquisition strategies in spoken dialogue systems via user simulation
A spoken dialogue system (SDS) is an application that supports conversational interaction with a human to perform some task. SDSs are emerging as an intuitive and efficient means for accessing information. A critical barrier to their widespread deployment remains in the form of communication …
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Reinforcement Learning and Reward Estimation for Dialogue Policy Optimisation
Modelling dialogue management as a reinforcement learning task enables a system to learn to act optimally by maximising a reward function. This reward function is designed to induce the system behaviour required for goal-oriented applications, which usually means fulfilling the user’s goal as …
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Incremental speech understanding in a multimodal web-based spoken dialogue system
In most spoken dialogue systems, the human speaker interacting with the system must wait until after finishing speaking to find out whether his or her speech has been accurately understood. The verbal and nonverbal indicators of understanding typical in human-to-human interaction are generally …
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Learning optimal discourse strategies in a spoken dialogue system
… human users and natural language agents. A spoken dialogue agent, ELVIS, is implemented as a testbed for learning optimal discourse strategies. ELVIS provides telephone-based voice access to a caller's email. Within ELVIS, various discourse strategies for the distribution of initiative, …
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Using question-specific vocabularies to support speech data collection with SALAAM
… has been an increasing use of small-vocabulary spoken dialogue systems in low-resource settings for information dissemination and data collection. This provides an opportunity to reduce the information gap in low-resource settings in which low-literacy is a huge hindrance to the adoption of …
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Data-Driven Policy Optimisation for Multi-Domain Task-Oriented Dialogue
… has opened new frontiers for conversational systems. Nevertheless, building data-driven multi-domain conversational agents that act optimally given a dialogue context is an open challenge. The first step towards that goal is developing an efficient way of learning a dialogue policy in new …