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- W2798367796 abstract "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%" @default.
- W2798367796 created "2018-05-07" @default.
- W2798367796 creator A5032046813 @default.
- W2798367796 creator A5059955534 @default.
- W2798367796 creator A5077994189 @default.
- W2798367796 date "2018-01-01" @default.
- W2798367796 modified "2023-10-13" @default.
- W2798367796 title "Global-Locally Self-Attentive Encoder for Dialogue State Tracking" @default.
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- W2798367796 doi "https://doi.org/10.18653/v1/p18-1135" @default.
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