@inproceedings{zheng-etal-2022-hit,
title = "{HIT}-{SCIR} at {MMNLU}-22: Consistency Regularization for Multilingual Spoken Language Understanding",
author = "Zheng, Bo and
Li, Zhouyang and
Wei, Fuxuan and
Chen, Qiguang and
Qin, Libo and
Che, Wanxiang",
editor = "FitzGerald, Jack and
Rottmann, Kay and
Hirschberg, Julia and
Bansal, Mohit and
Rumshisky, Anna and
Peris, Charith and
Hench, Christopher",
booktitle = "Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.mmnlu-1.4",
doi = "10.18653/v1/2022.mmnlu-1.4",
pages = "35--41",
abstract = "Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose to use consistency regularization based on a hybrid data augmentation strategy. The consistency regularization enforces the predicted distributions for an example and its semantically equivalent augmentation to be consistent. We conduct experiments on the MASSIVE dataset under both full-dataset and zero-shot settings. Experimental results demonstrate that our proposed method improves the performance on both intent detection and slot filling tasks. Our system ranked 1st in the MMNLU-22 competition under the full-dataset setting.",
}
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<abstract>Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose to use consistency regularization based on a hybrid data augmentation strategy. The consistency regularization enforces the predicted distributions for an example and its semantically equivalent augmentation to be consistent. We conduct experiments on the MASSIVE dataset under both full-dataset and zero-shot settings. Experimental results demonstrate that our proposed method improves the performance on both intent detection and slot filling tasks. Our system ranked 1st in the MMNLU-22 competition under the full-dataset setting.</abstract>
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%0 Conference Proceedings
%T HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding
%A Zheng, Bo
%A Li, Zhouyang
%A Wei, Fuxuan
%A Chen, Qiguang
%A Qin, Libo
%A Che, Wanxiang
%Y FitzGerald, Jack
%Y Rottmann, Kay
%Y Hirschberg, Julia
%Y Bansal, Mohit
%Y Rumshisky, Anna
%Y Peris, Charith
%Y Hench, Christopher
%S Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F zheng-etal-2022-hit
%X Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling. To improve the performance of these two sub-tasks, we propose to use consistency regularization based on a hybrid data augmentation strategy. The consistency regularization enforces the predicted distributions for an example and its semantically equivalent augmentation to be consistent. We conduct experiments on the MASSIVE dataset under both full-dataset and zero-shot settings. Experimental results demonstrate that our proposed method improves the performance on both intent detection and slot filling tasks. Our system ranked 1st in the MMNLU-22 competition under the full-dataset setting.
%R 10.18653/v1/2022.mmnlu-1.4
%U https://aclanthology.org/2022.mmnlu-1.4
%U https://doi.org/10.18653/v1/2022.mmnlu-1.4
%P 35-41
Markdown (Informal)
[HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding](https://aclanthology.org/2022.mmnlu-1.4) (Zheng et al., MMNLU 2022)
ACL