To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) mannequin to explicitly consider slot correlations throughout completely different domains. Specially, we first apply a Slot Attention to learn a set of slot-particular options from the unique dialogue and then integrate them utilizing a slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi Guo creator Siqi Zhu creator 2020-nov text Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online conference publication Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). On this paper, we suggest a brand new structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang author Chencai Chen writer Liang He writer Zhou Yu writer 2020-jul textual content Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online convention publication Dialogue state tracker is chargeable for inferring consumer intentions by means of dialogue history. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to cut back redundant information’s interference and enhance long dialogue context monitoring.
