Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation (D19-55)
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| Challenge: | Neural Machine Translation models are sensitive to noise in the input data. |
| Approach: | They propose new methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. |
| Outcome: | The proposed methods extend limited noisy data and improve robustness to noise while keeping the models small. |
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| Challenge: | Neural machine translation models are sensitive to noises in input sentences . one special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. |
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| Challenge: | Existing approaches to domain adaptation for NMT depend on high-quality parallel data. |
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| Challenge: | A single sentence does not always convey information that is enough to translate it into other languages. |
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| Challenge: | Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation models. |
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| Challenge: | Recent advances in NMT have improved translation quality but are vulnerable to input perturbations. |
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Multimodal Robustness for Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing approaches to deal with noisy multimodal inputs are not robust enough to deal effectively with noisy data. |
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| Challenge: | Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed . |
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| Challenge: | Prior work treats all types of mismatches between source and target as noise . Consequently, it remains unclear how noisy parallel training samples impact NMT training. |
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