Papers with GLAD
Improving Dialogue State Tracking by Discerning the Relevant Context (N19-1)
Copied to clipboard
| Challenge: | Dialog state tracking (DST) is used to estimate user's goals and requests in order to plan next action and respond accordingly. |
| Approach: | They propose a framework that uses the current user utterance and the most recent system utterant to determine the relevance of a system . Specifically, they use the current and most recent user . and system adverbs to determine relevance. |
| Outcome: | The proposed framework improves goal accuracy by 2.75% and 2.36% on WoZ 2.0 and Multi-WoZ restaurant domain datasets over the previous state-of-the-art GLAD model. |
Global-Locally Self-Attentive Encoder for Dialogue State Tracking (P18-1)
Copied to clipboard
| 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%. |