SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Jiaying Hu, Yan Yang, Chengcai Chen, Liang He, Zhou Yu
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. The sharing improve the models ability to deduce value from related slots. Our model yields a significantly improved performance compared to previous state-of-the-art models on the Multi-WOZ dataset.