Global-Locally Self-Attentive Encoder for Dialogue State Tracking
Victor W. Zhong, Caiming Xiong, Richard Socher · 2018
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of taskoriented 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 share 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 and achieves stateof-the-art performance on the WoZ and DSTC2 state tracking tasks.GLAD obtains 88.1% joint goal accuracy and 97.1% request accuracy on WoZ, outperforming prior work by 3.7% and 5.5%.On DSTC2, our model obtains 74.5% joint goal accuracy and 97.5% request accuracy, outperforming prior work by 1.1% and 1.0%.