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To handle these phenomena, we propose 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 author Yi Guo creator Siqi Zhu author 2020-nov text 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 problems of dialogue state tracking (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson writer Adrian Cheung author 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) conference publication Slot-filling models in task-pushed dialog techniques rely on rigorously annotated training data. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He author Jinchao Zhang writer Yuanmeng Yan creator Weiran Xu writer Cheng Niu author Jie Zhou author 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 knowledge scarcity in goal domains.
SAS: Dialogue State Tracking by way of Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang creator Chencai Chen writer Liang He creator 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 responsible for inferring user intentions by means of dialogue historical past. Specially, we first apply a Slot Attention to learn a set of slot-particular features from the original dialogue and then combine them using a slot info sharing module. On this paper, we suggest a 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 scale back redundant information’s interference and improve lengthy dialogue context monitoring. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang creator Moxin Chen author Xinyu Dai author Yinggong Zhao author Shujian Huang author 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). While science might have disproven the link between wet heads and catching colds, it's still no fun to sit down around with a head filled with wet hair, particularly in the winter.
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