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الخبراء والأخطاء والسياق: دراسة واسعة النطاق للتقييم البشري للترجمة الآلية
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التقييم البشري التجريدي لأنظمة الترجمة عالية الجودة الحديثة هي مشكلة صعبة، وهناك أدلة متزايدة على أن إجراءات التقييم غير الكافية يمكن أن تؤدي إلى استنتاجات خاطئة. بينما كان هناك بحث كبير في التقييم البشري، لا يزال الحقل يفتقر إلى إجراء قياسي شائع. كخطوة نحو هذا الهدف، نقترح منهجية تقييم في تحليل خطأ صريح، استنادا إلى إطار مقاييس الجودة متعددة الأبعاد (MQM). نحن نفذت أكبر دراسة بحثية MQM حتى الآن، وتسجيل مخرجات الأنظمة العليا من المهمة المشتركة WMT 2020 في أزواج لغتين باستخدام التعليقات التوضيحية المقدمة من المترجمين المحترفين مع الوصول إلى سياق المستند الكامل. نقوم بتحليل البيانات الناتجة على نطاق واسع، والعثور على نتائج أخرى بمثابة ترتيب مختلف تماما للأنظمة المقدرة من تلك المنشأة من قبل عمال الحشد WMT، تعرض تفضيل واضح لإخراج الإنسان على الجهاز. من المستغرب، نجد أيضا أن المقاييس التلقائية القائمة على المدينات المدربة مسبقا يمكن أن تفوق عمال الحشد البشري. نحن نجعل كوربوس متاحة علنا لمزيد من البحث.
Abstract Human evaluation of modern high-quality machine translation systems is a difficult problem, and there is increasing evidence that inadequate evaluation procedures can lead to erroneous conclusions. While there has been considerable research on human evaluation, the field still lacks a commonly accepted standard procedure. As a step toward this goal, we propose an evaluation methodology grounded in explicit error analysis, based on the Multidimensional Quality Metrics (MQM) framework. We carry out the largest MQM research study to date, scoring the outputs of top systems from the WMT 2020 shared task in two language pairs using annotations provided by professional translators with access to full document context. We analyze the resulting data extensively, finding among other results a substantially different ranking of evaluated systems from the one established by the WMT crowd workers, exhibiting a clear preference for human over machine output. Surprisingly, we also find that automatic metrics based on pre-trained embeddings can outperform human crowd workers. We make our corpus publicly available for further research.
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