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Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi Guo author 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 issues of dialogue state tracking (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson writer Adrian Cheung creator Anish Mahendran creator Kevin Leach author Jonathan K Kummerfeld author 2020-dec text Proceedings of the twenty eighth International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) convention publication Slot-filling models in job-pushed dialog programs depend on rigorously annotated coaching information. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He creator Jinchao Zhang author Yuanmeng Yan creator Weiran Xu author Cheng Niu author Jie Zhou writer 2020-dec textual content Proceedings of the twenty eighth International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) conference publication Zero-shot slot filling has broadly arisen to cope with information scarcity in goal domains.<br><br><br><br> SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang creator Chencai Chen writer 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 convention publication Dialogue state tracker is accountable for inferring consumer intentions by way of dialogue historical past. Specially, we first apply a Slot Attention to study a set of slot-specific options from the unique dialogue after which integrate them using a slot data sharing module. On this paper, we suggest a brand new architecture 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 cut back redundant information’s interference and improve long dialogue context monitoring. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang writer Moxin Chen author Xinyu Dai creator Yinggong Zhao writer Shujian Huang writer Jiajun Chen writer 2020-jul textual content 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 tracking (DST). 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