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Register a new userScrambled Translation Problem: A Problem of Denoising UNMT
مشكلة الترجمة المختبرة: مشكلة Denoising UNMT
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في هذه الورقة، نحدد نوعا مثيرا للاهتمام من الخطأ في إخراج أنظمة الترجمة الآلية العصبية غير الخاضعة للكشف عنها مثل Undreamt1. نشير إلى نوع الخطأ هذا كمحالة ترجمة مدخبة. نلاحظ أن نماذج UNMT التي تستخدم ضوضاء خلط ورق اللعب الكلمة (كما هو الحال في حالة UNTreamt) يمكن أن تولد كلمات صحيحة ولكنها تفشل في غرزة معا لتشكيل العبارات. نتيجة وكلمات الجملة المترجمة تبدو سارعت وانخفاض بلو. نحن نفترض أن السبب وراء مشكلة الترجمة المخفوقة هي "خلط الضوضاء" التي يتم تقديمها في كل جملة مدخلات كاستراتيجية دنيوية. لاختبار فرضيتنا ونحن نجيب من خلال إعادة تدريب نماذج بعثة الأمم المتحدة في غول الصين نقوم بتوقف عن تدريب نموذج Denoising UNMT بعد قررت التكرارات مسبقا واستئناف التدريب من أجل التكرارات المتبقية - أي رقم هو أيضا قررت مسبقا - باستخدام الجملة الأصلية كمدخل دون إضافة أي ضجيج. يحقق حلنا المقترح نماذج UNMT تحسين الأداء التي تتدرب تقليديا. نوضح هذه المكاسب الأداء في أربع أزواج ولغوية وبيزن. والإنجليزية-الفرنسية والإنجليزية والألمانية والإنجليزية-الإسبانية والبنجابية الهندية. يوضح تحليلنا النوعي والكمي أن استراتيجية إعادة التدريب يساعد على تحقيق محاذاة أفضل كما لوحظ من خلال الاهتمام Heatmap والترجمة الجملية الأفضل وأدى إلى تحسين إحصائيا في درجات بلو.
In this paper and we identify an interesting kind of error in the output of Unsupervised Neural Machine Translation (UNMT) systems like Undreamt1. We refer to this error type as Scrambled Translation problem. We observe that UNMT models which use word shuffle noise (as in case of Undreamt) can generate correct words and but fail to stitch them together to form phrases. As a result and words of the translated sentence look scrambled and resulting in decreased BLEU. We hypothesise that the reason behind scrambled translation problem is 'shuffling noise' which is introduced in every input sentence as a denoising strategy. To test our hypothesis and we experiment by retraining UNMT models with a simple retraining strategy. We stop the training of the Denoising UNMT model after a pre-decided number of iterations and resume the training for the remaining iterations- which number is also pre-decided- using original sentence as input without adding any noise. Our proposed solution achieves significant performance improvement UNMT models that train conventionally. We demonstrate these performance gains on four language pairs and viz. and English-French and English-German and English-Spanish and Hindi-Punjabi. Our qualitative and quantitative analysis shows that the retraining strategy helps achieve better alignment as observed by attention heatmap and better phrasal translation and leading to statistically significant improvement in BLEU scores.
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