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A spoken dialog system, while communicating with a user, must keep track of what the user wants from the system at each step. This process, termed dialog state tracking, is essential for a successful dialog system as it directly informs the system's actions. The first Dialog State Tracking Challenge allowed for evaluation of different dialog state tracking(More)
In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence the state of the conversation can never be known with certainty. Much of the research in spoken dialog systems centres on mitigating this uncertainty and recent work has focussed(More)
In a spoken dialog system, dialog state tracking deduces information about the user's goal as the dialog progresses, synthesizing evidence such as dialog acts over multiple turns with external data sources. Recent approaches have been shown to overcome ASR and SLU errors in some applications. However, there are currently no common testbeds or evaluation(More)
Reinforcement learning (RL) is a promising technique for creating a dialog manager. RL accepts features of the current dialog state and seeks to find the best action given those features. Although it is often easy to posit a large set of potentially useful features, in practice, it is difficult to find the subset which is large enough to contain useful(More)
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(More)
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(More)
Whereas traditional dialog systems operate on the top ASR hypothesis, statistical dialog systems claim to be more robust to ASR errors by maintaining a distribution over multiple hidden dialog states. Recently, these techniques have been deployed publicly for the first time, making empirical measurements possible. In this paper, we analyze two of these(More)
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(More)
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(More)