To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) mannequin to explicitly consider slot correlations throughout different domains. Specially, we first apply a Slot Attention to study a set of slot-specific options from the original dialogue and then combine them using a slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang creator Yi Guo writer Siqi Zhu creator 2020-nov textual content Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online convention publication Incompleteness of domain ontology and unavailability of some values are two inevitable issues of dialogue state monitoring (DST). In this paper, we propose a new structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN. SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang creator Chencai Chen creator Liang He author Zhou Yu writer 2020-jul text Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online conference publication Dialogue state tracker is liable for inferring consumer intentions by means of dialogue history. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to scale back redundant information’s interference and improve long dialogue context monitoring.
发表于 2022-8-18 10:17:51
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