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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 totally different domains. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang creator Yi Guo author Siqi Zhu writer 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 issues of dialogue state monitoring (DST). Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods Stefan Larson creator Adrian Cheung author Anish Mahendran creator Kevin Leach writer Jonathan K Kummerfeld creator 2020-dec text Proceedings of the twenty eighth International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) convention publication Slot-filling models in job-pushed dialog programs depend on fastidiously annotated training information. Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack Keqing He author Jinchao Zhang writer Yuanmeng Yan writer Weiran Xu writer Cheng Niu creator Jie Zhou writer 2020-dec text Proceedings of the 28th International Conference on Computational Linguistics International Committee on Computational Linguistics Barcelona, Spain (Online) convention publication Zero-shot slot filling has extensively arisen to cope with knowledge scarcity in target domains.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang creator Chencai Chen author 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 convention publication Dialogue state tracker is responsible for inferring user intentions by dialogue historical past. Specially, we first apply a Slot Attention to study a set of slot-specific features from the original dialogue after which integrate them utilizing a slot data sharing module. On this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN. We suggest 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 creator Xinyu Dai author Yinggong Zhao writer Shujian Huang creator Jiajun Chen author 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 is nonetheless no fun to sit around with a head filled with wet hair, especially within the winter.
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