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Register a new userEnsemble Fine-tuned mBERT for Translation Quality Estimation
الفرقة التي tuned tuned mbert لتقدير جودة الترجمة
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يعد تقدير الجودة (QE) مكونا هاما لسير عمل الترجمة الآلي لأنه يقيم جودة الإخراج المترجم دون الترجمات المرجعية الاستشارية.في هذه الورقة، نناقش التقديم لدينا إلى المهمة المشتركة WMT 2021 QE.إننا نشارك في المهمة الفرعية الفرعية على مستوى الجملة 2 المهام التي تتحدى المشاركين للتنبؤ بدرجة HTER من أجل جهد التحرير على مستوى الجملة.نظامنا المقترح هو مجموعة من نماذج الانحدار من بيرت (mbert) متعددة اللغات، والتي يتم إنشاؤها بواسطة ضبط صقلها على إعدادات الإدخال المختلفة.يوضح أداء قابلا للمقارنة فيما يتعلق بترابط بيرسون، وتغلب على نظام الأساس في ماي / رموز لعدة أزواج اللغة.بالإضافة إلى ذلك، نقوم بتكييف نظامنا لإعداد اللقطة الصفرية من خلال استغلال أزواج اللغة ذات الصلة بالغة والترجمات المرجعية الزائفة.
Quality Estimation (QE) is an important component of the machine translation workflow as it assesses the quality of the translated output without consulting reference translations. In this paper, we discuss our submission to the WMT 2021 QE Shared Task. We participate in Task 2 sentence-level sub-task that challenge participants to predict the HTER score for sentence-level post-editing effort. Our proposed system is an ensemble of multilingual BERT (mBERT)-based regression models, which are generated by fine-tuning on different input settings. It demonstrates comparable performance with respect to the Pearson's correlation, and beat the baseline system in MAE/ RMSE for several language pairs. In addition, we adapt our system for the zero-shot setting by exploiting target language-relevant language pairs and pseudo-reference translations.
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