SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Jiaying Hu, Yan T. Yang, Chengcai Chen, Liang He, Zhou Yu · 2020
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.