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    <br> To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) model 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 creator 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 domain 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 writer Kevin Leach author Jonathan K Kummerfeld creator 2020-dec textual content Proceedings of the 28th International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) conference publication Slot-filling fashions in job-pushed dialog systems rely on fastidiously annotated coaching information. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He author Jinchao Zhang author Yuanmeng Yan author Weiran Xu author Cheng Niu writer Jie Zhou creator 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 knowledge scarcity in goal domains.<br>

    <br> SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang writer Chencai Chen creator Liang He author 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 answerable for inferring user intentions by dialogue history. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue after which integrate them using a slot information sharing module. In 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 cut back redundant information’s interference and improve long dialogue context tracking. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang writer Moxin Chen author Xinyu Dai author 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 convention publication Recent proposed approaches have made promising progress in dialogue state monitoring (DST). While science may have disproven the link between wet heads and catching colds, it’s nonetheless no enjoyable to sit round with a head full of wet hair, especially in the winter.<br>

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