COREDIAL
Spoken dialog management that combines corpus-based statistical learning and reinforcement learning with a constraint-based core
| Coordinatore | UNIVERSITAET POTSDAM
Organization address
address: AM NEUEN PALAIS 10 contact info |
| Nazionalità Coordinatore | Germany [DE] |
| Totale costo | 45˙000 € |
| EC contributo | 45˙000 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2010-RG |
| Funding Scheme | MC-ERG |
| Anno di inizio | 0 |
| Periodo (anno-mese-giorno) | 0000-00-00 - 0000-00-00 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
UNIVERSITAET POTSDAM
Organization address
address: AM NEUEN PALAIS 10 contact info |
DE (POTSDAM) | coordinator | 45˙000.00 |
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Obiettivo del progetto (Objective)
'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.'