On Romanization for Model Transfer Between Scripts in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Using romanization to improve low-resource machine translation is not always the best strategy. |
| Approach: | They propose to use romanization to improve transfer between languages with different scripts . they compare two romanization tools and find that they exhibit different degrees of information loss, which affects translation quality. |
| Outcome: | The proposed method improves transfer between languages with different scripts while entails information loss. |
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| Challenge: | a recent study has focused on setups that favor romanization for cross-lingual transfer . a fidelity-based approach is needed to improve performance for high-resource languages . |
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| Challenge: | Existing models for high-resource languages are not available for all languages, and the vast majority of the world's languages are excluded from these models. |
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Exploring and Predicting Transferability across NLP Tasks (2020.emnlp-main)
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Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
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Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies (P19-1)
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| Challenge: | Existing approaches to transfer a pretrained NMT model to a new, unrelated language without shared vocabularies are limited to cognate languages. |
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