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Register a new userData 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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في هذه الورقة، نحقق في عوامل القيادة وراء التسلسل، وهي طريقة بسيطة ولكنها فعالة من البيانات للترجمة الآلية العصبية منخفضة الموارد.تشير تجاربنا إلى أن سياق الخطاب غير مرجح هو سبب تحسين تسلسل بلو من قبل حوالي +1 عبر أربع أزواج لغوية.بدلا من ذلك، نوضح أن التحسن يأتي من ثلاثة عوامل أخرى لا علاقة لها بالحبال: تنوع السياق، وتنوع الطول، و (إلى حد أقل) يتحول الموقف.
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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