Dialog Systems based on Markov decision processes over two real tasks

In this work the state of the art of the automatic dialogue strategy management using Markov decision processes (MDP) with reinforcement learning (RL) is described. Partially observable Markov decision processes (POMDP) are also described. To test the validity of these methods, two spoken dialogue systems have been developed. The first one is a spoken dialogue system for weather forecast providing, and the second one is a more complex system for train information. With the first system, comparisons between a rule-based system and an automatically trained system have been done, using a real corpus to train the automatic strategy. In the second system, the scalability of these methods when used in larger systems has been tested.

Scaling POMDPs for dialog management with composite summary point-based value iteration (CSPBVI)

Although partially observable Markov decision processes (POMDPs) have shown great promise as a framework for dialog management in spoken dialog systems, important scalability issues remain. This paper tackles the problem of scaling slot-filling POMDP-based dialog managers to many slots with a novel technique called composite point-based value iteration (CSPBVI). CSPBVI creates a "local" POMDP policy for each slot; at runtime, each slot nominates an action and a heuristic chooses which action to take. Experiments in dialog simulation show that CSPBVI successfully scales POMDP-based dialog managers without compromising performance gains over baseline techniques and preserving robustness to errors in user model estimation. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.

Spoken Dialog Challenge 2010: Comparison of live and control test results

The Spoken Dialog Challenge 2010 was an exercise to investigate how different spoken dialog systems perform on the same task. The existing Let's Go Pittsburgh Bus Information System was used as a task and four teams provided systems that were first tested in controlled conditions with speech researchers as users. The three most stable systems were then deployed to real callers. This paper presents the results of the live tests, and compares them with the control test results. Results show considerable variation both between systems and between the control and live tests. Interestingly, relatively high task completion for controlled tests did not always predict relatively high task completion for live tests. Moreover, even though the systems were quite different in their designs, we saw very similar correlations between word error rate and task completion for all the systems. The dialog data collected is available to the research community. © 2011 Association for Computational Linguistics.

POMDP-based statistical spoken dialog systems: A review

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech recognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a framework. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems. © 1963-2012 IEEE.

Junge Hochschul- und Mediendidaktik. Forschung und Praxis im Dialog

Am 29./30.05.2012 fand in Hamburg die Konferenz “Junges Forum Hochschul- und Mediendidaktik” (JFHM) statt. Ausgerichtet vom Zentrum für Hochschul- und Weiterbildung (ZHW) der Universität Hamburg, kooperierten bei der Konzeption und Durchführung der Tagung Vertreterinnen und Vertreter aus hochschul- und mediendidaktischer Berufspraxis mit Vertreterinnen der wissenschaftlichen Nachwuchsförderung aus der Deutschen Gesellschaft für Hochschuldidaktik (DGHD) und der Gesellschaft für Medien in der Wissenschaft (GMW). Das Ziel der Tagung war die Sichtbarmachung und Vernetzung theoretischer und praktischer hochschul- und mediendidaktischer Arbeit. Der vorliegende Sammelband vereint Beiträge der Konferenz und gibt so einen Einblick in aktuelle Themen von Hochschul- und Mediendidaktik - und zwar speziell aus der Perspektive jüngerer Forscherinnen und Forscher sowie Praktikerinnen und Praktiker. Er gibt damit auch Anhaltspunkte dafür, welche Themen diese Arbeitsbereiche in Zukunft (weiter) beschäftigen werden. (DIPF/Autor)

Chapter 2: Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis

Nistor, N., Dascalu, M., Stavarache, L.L., Tarnai, C., & Trausan-Matu, S. (2015). Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis. In Y. Li, M. Chang, M. Kravcik, E. Popescu, R. Huang, Kinshuk & N.-S. Chen (Eds.), State-of-the-Art and Future Directions of Smart Learning (Vol. Lecture Notes in Educational Technology, pp. 13–17). Berlin, Germany: Springer-Verlag Singapur

Dialog??ndo se resuelven los conflictos

resumen tomado del autor

Academic integrity guidelines from Dialog Plus

The Academic Integrity Guidelines (AIG), initally designed at Penn State University, is a small nugget from the JISC Project Dialog Plus which tests students' understanding of good academic integrity. The nugget presents students with an overview of their own institution's integrity guidelines before moving onto completing a set of multiple choice questions. This particular nugget has proved so popular that it has been exported from PSU and embedded into courses at both Leeds and Southampton.