Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

Yingmei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang


Abstract
Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.
Anthology ID:
2021.emnlp-main.259
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3226–3237
Language:
URL:
https://aclanthology.org/2021.emnlp-main.259
DOI:
10.18653/v1/2021.emnlp-main.259
Bibkey:
Cite (ACL):
Yingmei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, and Daxin Jiang. 2021. Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3226–3237, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding (Guo et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.259.pdf