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Register a new userSelecting the best data filtering method for NMT training
اختيار أفضل طريقة تصفية البيانات لتدريب NMT
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ثبت أن أداء أنظمة NMT يعتمد على جودة بيانات التدريب.في هذه الورقة، نستكشف أدوات مختلفة مفتوحة المصدر التي يمكن استخدامها لتسجيل جودة أزواج الترجمة، بهدف الحصول على كورسا نظيفة لتدريب نماذج NMT.نقيس أداء هذه الأدوات من خلال ربط درجاتهم بالدرجات البشرية، وكذلك نماذج الرتبة المدربة على مجموعات البيانات التي تمت تصفيتها الناتجة من حيث أدائها في مجموعات اختبار مختلفة ومقاييس أداء MT.
Performance of NMT systems has been proven to depend on the quality of the training data. In this paper we explore different open-source tools that can be used to score the quality of translation pairs, with the goal of obtaining clean corpora for training NMT models. We measure the performance of these tools by correlating their scores with human scores, as well as rank models trained on the resulting filtered datasets in terms of their performance on different test sets and MT performance metrics.
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Data filtering for machine translation (MT) describes the task of selecting a subset of a given, possibly noisy corpus with the aim to maximize the performance of an MT system trained on this selected data. Over the years, many different filtering ap
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