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Spoken dialog management that combines corpus based statistical learning and rei...

Spoken dialog management that combines corpus based statistical learning and reinforcement learning with a constraint based core Spoken dialog systems (SDS) have made great progress over the last 10 years. Their use has become wide-spread in many areas, especially call center automation, but also for interacting with car- based dialog systems or robots, for... ver más

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45K€

Presupuesto del proyecto: 45K€

Líder del proyecto

UNIVERSITAET POTSDAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto No tenemos la información de la convocatoria

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Líder del proyecto

UNIVERSITAET POTSDAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 45K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Spoken dialog systems (SDS) have made great progress over the last 10 years. Their use has become wide-spread in many areas, especially call center automation, but also for interacting with car- based dialog systems or robots, for example. Despite this progress, significant challenges remain: human-machine communication is in practice quite different from human-human communication. We will address dialog management, a core task for spoken dialog systems that deals with making an action decision, and extend the approach to include language understanding and generation in a complete spoken dialog system. The objective of CoreDial project is to provide a constructive proof that corpus-based statistical methods can be combined with reinforcement learning for dialog management. Both methods will be used as (re)ranking methods for lexically realized dialog moves that are generated by a constraint-based, overgenerating core system. This involves several components (and thus sub-goals/objectives): a) Constraint-based core dialog manager and generator using syntactic mechanisms to produce lexically realized dialog moves, b) Corpus-based statistical ranker employing a novel dialog language model that uses methods from statistical machine translation, c) Reinforcement Learning based (re)-ranker to optimize the overall dialog and in particular handle noise, e.g. deciding about clarification questions, d) Dialog system architecture and statistical model combination to integrate the components. This system and its evaluation will be the main result of the project. The Reintegration Grant will allow the applicant to integrate himself into the host organization, maintain links to the current Marie Curie host, and prepare himself for his future professional development.

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