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Dialog state tracking - the process of updating the dialog state after each interaction with the user - is a key component of most dialog systems. Following a similar scheme to the fourth dialog state tracking challenge, this edition again focused on human-human dialogs, but introduced the task of cross-lingual adaptation of trackers. The challenge received a total of 32 entries from 9 research groups...
This paper tackles the problem of learning a dialog policy from example dialogs - for example, from Wizard-of-Oz style dialogs, where an expert (person) plays the role of the system. Learning in this setting is challenging because dialog is a temporal process in which actions affect the future course of the conversation - i.e., dialog requires planning. Past work solved this problem with either conventional...
In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. This task is challenging because of speech recognition and language understanding errors, yet good dialog state tracking is crucial to the performance of spoken dialog systems. This paper presents results from the third Dialog State...
This paper examines two statistical spoken dialog systems deployed to the public, extending an earlier study on one system [1]. Results across the two systems show that statistical techniques improved performance in some cases, but degraded performance in others. Investigating degradations, we find the three main causes are (non-obviously) inaccurate parameter estimates, poor confidence scores, and...
Crowd-sourcing is a promising method for fast and cheap transcription of large volumes of speech data. However, this method cannot achieve the accuracy of expert transcribers on speech that is difficult to transcribe. Faced with such speech data, we developed three new methods of crowd-sourcing, which allow explicit trade-offs among precision, recall, and cost. The methods are: incremental redundancy,...
This paper is a demonstration of the AT&T "Let's Go" bus timetable spoken dialog system. The system was entered in the 2010 Spoken Dialog Challenge, where the task is to provide bus timetable information for Pittsburgh, Pennsylvania. Its primary aim was to build a statistical spoken dialog system to comemrcial production standards, both in terms of user interface, and also in terms of...
For spoken dialog systems, tracking a distribution over multiple dialog states has been shown to add robustness to speech recognition errors. To retain tractability, past work has suggested tracking dialog states in groups called partitions. While promising, current techniques are limited to incorporating a small number of ASR N-Best hypotheses. This paper overcomes this limitation by incrementally...
Although user simulations are increasingly employed in the development and assessment of spoken dialog systems, there is no accepted method for evaluating user simulations. In this paper, we propose a novel quality measure for user simulations. We view a user simulation as a predictor of the performance of a dialog system, where per-dialog performance is measured with a domain-specific scoring function...
The benefit of tracking a probability distribution over multiple dialogue states has been demonstrated in the literature. However, the dialogue state in past work has been limited to a small number of variables, and growing the number of variables in the dialogue state prevents the probability distribution from being updated in real-time. This paper shows how the number of variables composing the...
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