Automating spoken dialogue management design using machine learning: An industry perspective

Paek, Tim; Pieraccini, Roberto

doi:10.1016/j.specom.2008.03.010

Cited by 94 publications

(50 citation statements)

References 30 publications

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“…Finally, it should be acknowledged that the philosophy of this statistical approach to dialogue system design has been questioned by some industryfocussed practitioners on the grounds that it is inconsistent with the need to provide guarantees to service providers regarding specific system behaviours (Paek and Pieraccini, 2008). This is certainly a legitimate concern.…”

Section: Discussionmentioning

confidence: 99%

The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management

Young

Gašić

Keizer

et al. 2010

Computer Speech & Language

378

240

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Section: Discussionmentioning

confidence: 99%

The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management

Young

Gašić

Keizer

et al. 2010

Computer Speech & Language

378

240

View full text Add to dashboard Cite

“…There exists a reduced number of context-aware speech interfaces in the literature and they are usually applied to very specifi domains [114,129]. In our proposal we merge context-awareness with speech interfaces in order to obtain fully accessible and personalized web services and information in hand-held devices.…”

Section: Related Workmentioning

confidence: 99%

Modeling the user state for context-aware spoken interaction in ambient assisted living

2014

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Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfil these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influenc of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.

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“…Specifically, estimating the user model and the reward function is a significant challenge since these model components have a direct impact on the optimized dialogue strategy. Furthermore, the reward function is perhaps the most hand-crafted aspect of the optimization frameworks such as (PO)MDPs (Paek and Pieraccini, 2008). Using inverse reinforcement learning (IRL) techniques, a reward function can be determined from expert actions (such as caregiver actions) (Ng and Russell, 2000).…”

Section: Objectivementioning

confidence: 99%

Dialogue Strategy Learning in Healthcare: A Systematic Approach for Learning Dialogue Models from Data

Chinaei

Chaib-draa

2014

Proceedings of the 5th Workshop on Speech and Language Processing for Assistive Technologies

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We aim to build dialogue agents that optimize the dialogue strategy, specifically through learning the dialogue model components from dialogue data. In this paper, we describe our current research on automatically learning dialogue strategies in the healthcare domain. We go through our systematic approach of learning dialogue model components from data, specifically user intents and the user model, as well as the agent reward function. We demonstrate our experiments on healthcare data from which we learned the dialogue model components. We conclude by describing our current research for automatically learning dialogue features that can be used in representing dialogue states and learning the reward function.

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Automating spoken dialogue management design using machine learning: An industry perspective

Cited by 94 publications

References 30 publications

The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management

The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management

Modeling the user state for context-aware spoken interaction in ambient assisted living

Dialogue Strategy Learning in Healthcare: A Systematic Approach for Learning Dialogue Models from Data

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