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. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking, and also deeper understandings about the problem itself, including methods for evaluation.
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.
This paper introduces the Finite-State TurnTaking Machine (FSTTM), a new model to control the turn-taking behavior of conversational agents. Based on a non-deterministic finite-state machine, the FSTTM uses a cost matrix and decision theoretic principles to select a turn-taking action at any time. We show how the model can be applied to the problem of end-of-turn detection. Evaluation results on a deployed spoken dialog system show that the FSTTM provides significantly higher responsiveness than previous approaches.
This paper describes a novel algorithm to dynamically set endpointing thresholds based on a rich set of dialogue features to detect the end of user utterances in a dialogue system. By analyzing the relationship between silences in user's speech to a spoken dialogue system and a wide range of automatically extracted features from discourse, semantics, prosody, timing and speaker characteristics, we found that all features correlate with pause duration and with whether a silence indicates the end of the turn, with semantics and timing being the most informative. Based on these features, the proposed method reduces latency by up to 24% over a fixed threshold baseline. Offline evaluation results were confirmed by implementing the proposed algorithm in the Let's Go system.
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