Papers with Global-Locally Self-Attentive Dialogue State Tracker

1 papers
Global-Locally Self-Attentive Encoder for Dialogue State Tracking (P18-1)

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Challenge: a global-local self-attentive dialogue state tracker estimates user goals and requests given the dialogue context . GLAD significantly improves tracking of rare states, compared to prior work . task-oriented dialogue systems can significantly reduce operating costs .
Approach: They propose a global-local self-attentive dialogue state tracker which shares global-level modules with global-specific estimators for different types of dialogue states.
Outcome: The proposed model outperforms previous models on the WoZ state tracking task by 3.9% and 4.8%.

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