The use of discriminative belief tracking in POMDP-based dialogue systems

Statistical spoken dialogue systems based on Partially Ob-servable Markov Decision Processes (POMDPs) have been shown to be more robust to speech recognition errors by main-taining a belief distribution over multiple dialogue states and making policy decisions based on the entire distribution rather than the single most likely hypothesis. To date most POMDP-based systems have used generative trackers. However, con-cerns about modelling accuracy have created interest in dis-criminative methods, and recent results from the second Dia-log State Tracking Challenge (DSTC2) have shown that dis-criminative trackers can significantly outperform generative models in terms of tracking accuracy. The aim of this pa-per is to investigate the extent to which these improvements translate into improved task completion rates when incorpo-rated into a spoken dialogue system. To do this, the Recur-rent Neural Network (RNN) tracker described by Henderson et al in DSTC2 was integrated into the Cambridge statistical dialogue system and compared with the existing generative Bayesian network tracker. Using a Gaussian Process (GP) based policy, the experimental results indicate that the system using the RNN tracker performs significantly better than the system with the original Bayesian network tracker. Index Terms — dialogue management, spoken dialogue systems, recurrent neural networks, belief tracking, POMDP 1

The Dialog State Tracking Challenge Series: A Review

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn.  Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress.  The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker.  This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled

A framework for improving error detection and correction in spoken dialog systems

Despite The Recent Improvements In Performance And Reliably Of The Different Components Of Dialog Systems, It Is Still Crucial To Devise Strategies To Avoid Error Propagation From One Another. In This Paper, We Contribute A Framework For Improved Error Detection And Correction In Spoken Conversational Interfaces. The Framework Combines User Behavior And Error Modeling To Estimate The Probability Of The Presence Of Errors In The User Utterance. This Estimation Is Forwarded To The Dialog Manager And Used To Compute Whether It Is Necessary To Correct Possible Errors. We Have Designed An Strategy Differentiating Between The Main Misunderstanding And Non-Understanding Scenarios, So That The Dialog Manager Can Provide An Acceptable Tailored Response When Entering The Error Correction State. As A Proof Of Concept, We Have Applied Our Proposal To A Customer Support Dialog System. Our Results Show The Appropriateness Of Our Technique To Correctly Detect And React To Errors, Enhancing The System Performance And User Satisfaction.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)

A proposal to manage multi-task dialogs in conversational interfaces

The emergence of smart devices and recent advances in spoken language technology are currently extending the use of conversational interfaces and spoken interaction to perform many tasks. The dialog management task of a conversational interface consists of selecting the next system response considering the user's actions, the dialog history, and the results of accessing the data repositories. In this paper we describe a dialog management technique adapted to multi-task conversational systems. In our proposal, specialized dialog models are used to deal with each specific subtask of dialog objective for which the dialog system has been designed. The practical application of the proposed technique to develop a dialog system acting as a customer support service shows that the use of these specialized dialog models increases the quality and number of successful interactions with the system in comparison with developing a single dialog model

Building multi-domain conversational systems from single domain resources

Current Advances In The Development Of Mobile And Smart Devices Have Generated A Growing Demand For Natural Human-Machine Interaction And Favored The Intelligent Assistant Metaphor, In Which A Single Interface Gives Access To A Wide Range Of Functionalities And Services. Conversational Systems Constitute An Important Enabling Technology In This Paradigm. However, They Are Usually Defined To Interact In Semantic-Restricted Domains In Which Users Are Offered A Limited Number Of Options And Functionalities. The Design Of Multi-Domain Systems Implies That A Single Conversational System Is Able To Assist The User In A Variety Of Tasks. In This Paper We Propose An Architecture For The Development Of Multi-Domain Conversational Systems That Allows: (1) Integrating Available Multi And Single Domain Speech Recognition And Understanding Modules, (2) Combining Available System In The Different Domains Implied So That It Is Not Necessary To Generate New Expensive Resources For The Multi-Domain System, (3) Achieving Better Domain Recognition Rates To Select The Appropriate Interaction Management Strategies. We Have Evaluated Our Proposal Combining Three Systems In Different Domains To Show That The Proposed Architecture Can Satisfactory Deal With Multi-Domain Dialogs. (C) 2017 Elsevier B.V. All Rights Reserved.Work partially supported by projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02

Bringing together commercial and academic perspectives for the development of intelligent AmI interfaces

The users of Ambient Intelligence systems expect an intelligent behavior from their environment, receiving adapted and easily accessible services and functionality. This can only be possible if the communication between the user and the system is carried out through an interface that is simple (i.e. which does not have a steep learning curve), fluid (i.e. the communication takes place rapidly and effectively), and robust (i.e. the system understands the user correctly). Natural language interfaces such as dialog systems combine the previous three requisites, as they are based on a spoken conversation between the user and the system that resembles human communication. The current industrial development of commercial dialog systems deploys robust interfaces in strictly defined application domains. However, commercial systems have not yet adopted the new perspective proposed in the academic settings, which would allow straightforward adaptation of these interfaces to various application domains. This would be highly beneficial for their use in AmI settings as the same interface could be used in varying environments. In this paper, we propose a new approach to bridge the gap between the academic and industrial perspectives in order to develop dialog systems using an academic paradigm while employing the industrial standards, which makes it possible to obtain new generation interfaces without the need for changing the already existing commercial infrastructures. Our proposal has been evaluated with the successful development of a real dialog system that follows our proposed approach to manage dialog and generates code compliant with the industry-wide standard VoiceXML.Research funded by projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), and DPS2008- 07029-C02-02.Publicad

DIALOG MANAGEMENT USING ACTIVE LEARNING ALGORITHMS

Master'sMASTER OF SCIENC