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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 throughout completely different domains. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang creator 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 convention 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 creator Adrian Cheung author Anish Mahendran writer Kevin Leach writer Jonathan K Kummerfeld author 2020-dec text Proceedings of the 28th International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) conference publication Slot-filling fashions in job-driven dialog programs rely on rigorously annotated coaching data. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He creator Jinchao Zhang writer 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 broadly 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 creator Chencai Chen writer 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 liable for inferring user intentions via dialogue history. Specially, we first apply a Slot Attention to be taught a set of slot-specific features from the unique dialogue after which integrate them utilizing a slot data sharing module. In this paper, we suggest 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 cut back redundant information’s interference and enhance lengthy dialogue context monitoring. Dialogue State Tracking with Explicit Slot Connection Modeling Yawen Ouyang author Moxin Chen creator Xinyu Dai author Yinggong Zhao writer Shujian Huang author Jiajun Chen creator 2020-jul text Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online conference publication Recent proposed approaches have made promising progress in dialogue state tracking (DST). While science could have disproven the link between wet heads and catching colds, it’s nonetheless no enjoyable to sit down around with a head full of wet hair, particularly in the winter.
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