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Register a new userIs This Translation Error Critical?: Classification-Based Human and Automatic Machine Translation Evaluation Focusing on Critical Errors
هل هذا خطأ الترجمة أمر بالغ الأهمية؟: تقييم الترجمة من الآلات والآلية القائمة على التصنيف يركز على الأخطاء الحرجة
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تناقش هذه الورقة نهجا قائما على التصنيف لتقييم الترجمة الآلي، بدلا من نهج قائم على الانحدار المشترك في مهمة مقاييس WMT.تعمل الترجمة الآلية الحديثة عادة بشكل جيد ولكن في بعض الأحيان تجعل الأخطاء الحرجة بسبب بعض خيارات كلمة خاطئة فقط.يركز نهجنا القائم على التصنيف على هذه الأخطاء باستخدام العديد من ملصقات نوع الخطأ، لتقييم ترجمة الآلات العملي في عصر الترجمة الآلية العصبية.لقد بذلنا شرحا إضافيا على مجموعات بيانات المقاييس 2015-2017 مع ملصقات الطلاقة والكفاية لتمييز أنواع مختلفة من أخطاء الترجمة من نقاط العرض النحوية والدلسة.نقدم معايير التقييم البشرية لدينا لتطوير Corpus وتجارب التقييم التلقائي باستخدام Corpus.سيتم إتاحة كوربوس التقييم البشري علنا عند النشر.
This paper discusses a classification-based approach to machine translation evaluation, as opposed to a common regression-based approach in the WMT Metrics task. Recent machine translation usually works well but sometimes makes critical errors due to just a few wrong word choices. Our classification-based approach focuses on such errors using several error type labels, for practical machine translation evaluation in an age of neural machine translation. We made additional annotations on the WMT 2015-2017 Metrics datasets with fluency and adequacy labels to distinguish different types of translation errors from syntactic and semantic viewpoints. We present our human evaluation criteria for the corpus development and automatic evaluation experiments using the corpus. The human evaluation corpus will be publicly available upon publication.
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