Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
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.
Cite
@article{arxiv.2109.01583,
title = {Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding},
author = {Yingmei Guo and Linjun Shou and Jian Pei and Ming Gong and Mingxing Xu and Zhiyong Wu and Daxin Jiang},
journal= {arXiv preprint arXiv:2109.01583},
year = {2021}
}
Comments
Long paper at EMNLP 2021