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To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) mannequin to explicitly consider slot correlations across completely different domains. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo writer Siqi Zhu creator 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 issues of dialogue state monitoring (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson author Adrian Cheung creator Anish Mahendran writer Kevin Leach creator Jonathan K Kummerfeld 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 Slot-filling models in activity-driven dialog programs rely on carefully annotated training knowledge. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He creator Jinchao Zhang author Yuanmeng Yan creator Weiran Xu writer Cheng Niu writer Jie Zhou author 2020-dec text Proceedings of the 28th International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) conference publication Zero-shot slot filling has extensively arisen to cope with information scarcity in goal domains.



SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang creator Chencai Chen creator Liang He creator Zhou Yu author 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 answerable for inferring person 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 utilizing a slot data sharing module. On this paper, we suggest a brand new structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and improve long dialogue context monitoring. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang creator Moxin Chen author Xinyu Dai writer Yinggong Zhao author Shujian Huang creator Jiajun Chen creator 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 tracking (DST). While science could 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, especially within the winter.



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