To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Specially, we first apply a Slot Attention to learn a set of slot-particular options from the unique dialogue after which integrate them using a slot data sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang creator Yi Guo writer Siqi Zhu writer 2020-nov textual content Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online conference publication Incompleteness of area ontology and unavailability of some values are two inevitable issues 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), known as SAVN. SAS: Dialogue State Tracking by way of Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang creator Chencai Chen author Liang He writer Zhou Yu author 2020-jul textual content Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online conference publication Dialogue state tracker is chargeable for inferring consumer intentions by dialogue history. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and improve lengthy dialogue context tracking.
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Topic: Five Important Methods To Slot Online (Read 124 times)