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آلة تصرف الترجمة
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يتطلب نشر الترجمة الآلية الناجحة (MT) فهم ليس فقط الصفات الجوهرية لإخراج MT، مثل الطلاقة وكفاية، ولكن أيضا تصورات المستخدمين.يستجيب المستخدمون الذين لا يفهمون لغة المصدر إخراج MT بناء على تصورهم للحصول على احتمال أن يطابق معنى إخراج MT معنى النص المصدر.نشير إلى ذلك باعتباره القدرة على الصيغة.قد يكون الإخراج غير القابل للصدق خارج المستخدمين، ولكن إخراج MT قابل للصدق مع معنى غير صحيح قد يضللها.في هذا العمل، ندرس علاقة المقابل بالطلاقة والكفااة من خلال تطبيق بروتوكولات التقييم المباشرة التقليدية التقليدية للتعليق على جميع الميزات الثلاثة على إخراج أنظمة MT العصبية.يوضح التحليل الكمي لهذه التعليقات التعليقات التوضيحية أن المعتقاة مرتبطة ارتباطا وثيقا من الطلاقة، ويقترح التحليل النوعي الأولي أن الميزات الدلالية قد حساب الفرق.
Successful Machine Translation (MT) deployment requires understanding not only the intrinsic qualities of MT output, such as fluency and adequacy, but also user perceptions. Users who do not understand the source language respond to MT output based on their perception of the likelihood that the meaning of the MT output matches the meaning of the source text. We refer to this as believability. Output that is not believable may be off-putting to users, but believable MT output with incorrect meaning may mislead them. In this work, we study the relationship of believability to fluency and adequacy by applying traditional MT direct assessment protocols to annotate all three features on the output of neural MT systems. Quantitative analysis of these annotations shows that believability is closely related to but distinct from fluency, and initial qualitative analysis suggests that semantic features may account for the difference.
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