Counterfactual Data Augmentation for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Neural machine translation models often rely on large-scale parallel corpora for training, exhibiting degraded performance on low-resource languages. |
| Approach: | They propose a method that interprets language models and phrasal alignment causally and generates augmented parallel translation corpora by sampling new source phrases from a masked language model. |
| Outcome: | The proposed method improves translation, backtranslation and translation robustness on IWSLT’15 English Vietnamese, WMT’17 English - German, and WMT'18 English – Turkish. |
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| Challenge: | Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words. |
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Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)
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| Challenge: | Existing methods for enhancing training data are limited in natural language tasks due to text characteristics. |
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| Challenge: | Recent neural machine translation models have improved translation quality but they also introduce small perturbations like misspelling and paraphrasing. |
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Manifold Adversarial Augmentation for Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing studies measure the superiority of DA methods in terms of their performance on a specific test set, but some do not exhibit consistent improvements across translation tasks. |
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Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Neural machine translation is known to show poor performance at long sentence translations . however, when the sentence length exceeds a certain value, the quality of NMT becomes inferior to that of statistical machine translation. |
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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. |
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Data augmentation using back-translation for context-aware neural machine translation (D19-65)
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| Challenge: | A single sentence does not always convey information that is enough to translate it into other languages. |
| Approach: | They obtain large-scale pseudo parallel corpora by back-translating monolingual data and examine their impact on translation accuracy. |
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Multilingual Unsupervised Neural Machine Translation with Denoising Adapters (2021.emnlp-main)
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| Challenge: | Multilingual unsupervised machine translation is a computationally expensive and hard to tune approach . auxiliary parallel data is used to train translation systems from monolingual data . |
| Approach: | They propose to use auxiliary parallel language pairs to train unsupervised machine translations . they propose to add auxiliary languages to pre-trained mBART-50 models with denoising adapters . |
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Improving Language Model Integration for Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing methods to integrate external language models into machine translation systems have been based on the assumption that the external model learns an implicit target-side language model at decoding time. |
| Approach: | They transfer this concept to the task of machine translation and compare it with the most prominent way of including additional monolingual data - namely back-translation. |
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