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To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) mannequin to explicitly consider slot เว็บตรง ฝากถอนไม่มีขั้นต่ํา correlations throughout totally different domains. Specially, we first apply a Slot Attention to be taught a set of slot-particular features from the original dialogue and then integrate them utilizing 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 domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). In this paper, we suggest a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. SAS: Dialogue State Tracking by way of Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang author Chencai Chen writer Liang He creator 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 chargeable for inferring person intentions by way of dialogue historical past. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to scale back redundant information’s interference and enhance lengthy dialogue context monitoring. |
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