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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 throughout totally different domains. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi Guo author 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 area ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson creator Adrian Cheung writer Anish Mahendran writer Kevin Leach creator Jonathan K Kummerfeld creator 2020-dec text Proceedings of the 28th International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) convention publication Slot-filling models in task-pushed dialog techniques depend on fastidiously annotated coaching information. 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 author Jie Zhou writer 2020-dec text 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 data scarcity in goal domains.
SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang author Chencai Chen creator Liang He writer Zhou Yu writer 2020-jul text Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online conference publication Dialogue state tracker is accountable for inferring person intentions via dialogue history. Specially, we first apply a Slot Attention to study a set of slot-particular features from the unique dialogue and then integrate them utilizing a slot data sharing module. In this paper, we propose a new architecture 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 enhance long dialogue context monitoring. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang writer Moxin Chen creator Xinyu Dai author Yinggong Zhao creator Shujian Huang writer Jiajun Chen writer 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 fun to sit round with a head full of wet hair, especially in the winter.
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