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Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution

تكبير البيانات عن طريق تسلسل للترجمة المنخفضة الموارد: لغز وحل

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation. Our experiments suggest that discourse context is unlikely the cause for concatenation improving BLEU by about +1 across four language pairs. Instead, we demonstrate that the improvement comes from three other factors unrelated to discourse: context diversity, length diversity, and (to a lesser extent) position shifting.



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