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)

ASR error management for improving spoken language understanding

This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201

INFRISK : a computer simulation approach to risk management in infrastructure project finance transactions

Few issues in modern finance have inspired the interest of both practitioners and theoreticians more than risk evaluation and management. The basic principle governing risk management in an infrastructure project finance deal is intuitive and well-articulated: allocate project-specific risks to parties best able to bear them (taking into account each party's appetite for, and aversion to, risk); control performance risk through incentives; and use market hedging instruments (derivatives) for covering marketwide risks arising from fluctuations in, for instance, interest and exchange rates, among other things. In practice, however, governments have been asked to provide guarantees for various kinds of projects, often at no charge, because of problems associated with market imperfections: a) Derivative markets (swaps, forwards) for currency and interest-rate risk hedging either do not exist or are inadequately developed in most developing countries. b) Limited contracting possibilities (because of problems with credibility of enforcement). c) Differing methods for risk measurement and evaluation. Two factors distinguish the financing of infrastructure projects from corporate and traditional limited-recourse project finance: 1) a high concentration of project risk early in the project life cycle (pre-completion), and 2) a risk profile that changes as the project comes to fruition, with a relatively stable cash flow subject to market and regulatory risk once the project is completed. The authors introduce INFRISK, a computer-based risk-management approach to infrastructure project transactions that involve the private sector. Developed in-house in the Economic Development Institute of the World Bank, INFRISK is a guide to practitioners in the field and a training tool for raising awareness and improving expertise in the application of modern risk management techniques. INFRISK can analyze a project's exposure to a variety of market, credit, and performance risks form the perspective of key contracting parties (project promoter, creditor, and government). Their model is driven by the concept of the project's economic viability. Drawing on recent developments in the literature on project evaluation under uncertainty, INFRISK generates probability distributions for key decision variables, such as a project's net present value, internal rate of return, or capacity to service its debt on time during the life of the project. Computationally, INFRISK works in conjunction with Microsoft Excel and supports both the construction and the operation phases of a capital investment project. For a particular risk variable of interest (such as the revenue stream, operations and maintenance costs, and construction costs, among others) the program first generates a stream of probability of distributions for each year of a project's life through a Monte Carlo simulation technique. One of the key contributions made by INFRISK is to enable the use of a broader set of probability distributions (uniform, normal, beta, and lognormal) in conducting Monte Carlo simulations rather than relying only on the commonly used normal distribution. A user's guide provides instruction on the use of the package.Banks&Banking Reform,Economic Theory&Research,Environmental Economics&Policies,Payment Systems&Infrastructure,Public Sector Economics&Finance,Financial Intermediation,Banks&Banking Reform,Environmental Economics&Policies,Economic Theory&Research,Public Sector Economics&Finance

Genetic Algorithm-Based Model for Determination of Efficient Management Strategies for Irrigation Canal Networks

An optimization model for the determination of efficient management strategies for an irrigation canal network is developed. The objective is to minimize the total water consumed while satisfying various system constraints. An unsteady flow model is used to simulate the flow in the network. A genetic algorithm- (GA-) based framework is used to solve the model. The suitable GA parameters that should be used within the model, as well as the performance of various constraint-handling techniques, are studied. Uncertainties in crop pattern and water consumption rates are incorporated into the search procedure to identify more reliable solutions. A graphical interface is also developed to make the model more user-friendly

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class