Addressing the Vulnerability of NMT in Input Perturbations (2021.naacl-industry)
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| Challenge: | Recent advances in NMT have improved translation quality but are vulnerable to input perturbations. |
| Approach: | They propose a method to reduce the effect of noisy inputs by using a Context-Enhanced Reconstruction approach. |
| Outcome: | The proposed approach improves robustness on Chinese-English and French-English translation tasks. |
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| Challenge: | Neural Machine Translation models are sensitive to noise in the input data. |
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| Challenge: | Recent work has shown that Neural Machine Translation models are brittle to small perturbations in the input. |
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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: | Neural machine translation (NMT) has weaknesses in handling lowfrequency and ambiguous words, which we refer to as troublesome words. |
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Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (P19-1)
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| Challenge: | Existing approaches to exploit sentential context for machine translation are not well studied. |
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| Challenge: | Existing (small) perturbations that induce a critical prediction error in machine learning models are often referred to as adversarial examples. |
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