SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Jiaying Hu, Yan Yang, Chencai Chen, liang he, Zhou Yu
Dialogue and Interactive Systems Long Paper
Session 11B: Jul 8
(06:00-07:00 GMT)
Session 12B: Jul 8
(09:00-10:00 GMT)
Abstract:
Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and improve long dialogue context tracking. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a slot information sharing module. Our model yields a significantly improved performance compared to previous state-of the-art models on the MultiWOZ dataset.
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