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Global-Locally Self-Attentive Encoder for Dialogue State Tracking

Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to shares parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states. GLAD obtains 88.3{\%} joint goal accuracy and 96.4{\%} request accuracy on the WoZ state tracking task, outperforming prior work by 3.9{\%} and 4.8{\%}. On the DSTC2 task, our model obtains 74.7{\%} joint goal accuracy and 97.3{\%} request accuracy, outperforming prior work by 1.3{\%} and 0.8{\%}

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