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Experimental results verify the benefits of explicit slot connection modeling, and our mannequin achieves state-of-the-artwork performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets. Empirical outcomes exhibit that SAVN achieves the state-of-the-art joint accuracy of 54.52% on MultiWOZ 2.0 and 54.86% on MultiWOZ 2.1. Besides, we evaluate VN with incomplete ontology. Experimental outcomes present that our model considerably outperforms state-of-the-artwork baselines below both zero-shot and few-shot settings. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) mannequin to explicitly consider slot correlations across totally different domains. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo writer Siqi Zhu author 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 area ontology and unavailability of some values are two inevitable issues of dialogue state monitoring (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson writer Adrian Cheung creator Anish Mahendran writer Kevin Leach writer Jonathan K Kummerfeld writer 2020-dec text Proceedings of the 28th International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) convention publication Slot-filling fashions in process-driven dialog systems rely on rigorously annotated training information. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He author Jinchao Zhang creator Yuanmeng Yan creator Weiran Xu author Cheng Niu writer Jie Zhou creator 2020-dec textual content Proceedings of the twenty eighth International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) convention publication Zero-shot slot filling has extensively arisen to cope with knowledge scarcity in goal domains. SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu author 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 accountable for inferring consumer intentions by way of dialogue historical past. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the unique dialogue and then integrate them using a slot data sharing module. On this paper, we propose a new structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to cut back redundant information’s interference and improve long dialogue context tracking. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang creator Moxin Chen creator Xinyu Dai creator Yinggong Zhao creator Shujian Huang creator Jiajun Chen writer 2020-jul text Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online convention publication Recent proposed approaches have made promising progress in dialogue state monitoring (DST). 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