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Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo creator Siqi Zhu writer 2020-nov text 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 problems of dialogue state monitoring (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson creator Adrian Cheung writer Anish Mahendran creator Kevin Leach creator Jonathan K Kummerfeld creator 2020-dec text Proceedings of the twenty eighth International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) conference publication Slot-filling models in task-pushed dialog systems rely on rigorously annotated training data. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He writer Jinchao Zhang creator Yuanmeng Yan writer Weiran Xu author Cheng Niu creator Jie Zhou creator 2020-dec text Proceedings of the 28th 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 data scarcity in target domains.<br><br><br><br> SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang writer Chencai Chen author Liang He creator 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 liable for inferring user intentions by means of dialogue history. Specially, we first apply a Slot Attention to be taught a set of slot-particular features from the unique dialogue after which integrate them using a slot info sharing module. On this paper, we propose a new structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and enhance long dialogue context tracking. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang writer Moxin Chen writer Xinyu Dai creator Yinggong Zhao author Shujian Huang writer Jiajun Chen creator 2020-jul text Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online conference publication Recent proposed approaches have made promising progress in dialogue state tracking (DST). 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