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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 creator 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 issues of dialogue state tracking (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson author Adrian Cheung creator Anish Mahendran creator Kevin Leach author Jonathan K Kummerfeld writer 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 activity-pushed dialog systems depend on carefully annotated training knowledge. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He author Jinchao Zhang creator Yuanmeng Yan writer 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) conference publication Zero-shot slot filling has widely arisen to cope with information scarcity in target domains.



SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang writer Chencai Chen author Liang He writer Zhou Yu creator 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 responsible for inferring consumer intentions by means of dialogue historical past. Specially, we first apply a Slot Attention to study a set of slot-specific features from the original dialogue and then combine them using a slot data sharing module. On this paper, we propose 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 author Moxin Chen writer Xinyu Dai author Yinggong Zhao creator Shujian Huang creator Jiajun Chen creator 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). While science may have disproven the hyperlink between wet heads and catching colds, it's still no enjoyable to sit around with a head stuffed with wet hair, particularly in the winter.



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