Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding (2021.emnlp-main)
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| Challenge: | Existing approaches to scale out spoken language understanding to low-resource languages are noisy. |
| Approach: | They propose a method for mitigating noise in augmented data by training models with augmented datasets. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two benchmark datasets. |
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