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Register a new userExplaining Errors in Machine Translation with Absolute Gradient Ensembles
شرح الأخطاء في الترجمة الآلية مع مجموعات التدرج المطلقة
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يركز البحث الحالي على تقدير الجودة لجهاز الترجمة الآلية على جودة الجملة للترجمات.باستخدام أساليب الشرح، يمكننا استخدام تقديرات الجودة هذه لتحديد خطأ مستوى Word على مستوى Word.في هذا العمل، نقارن تقنيات الشرح المختلفة والتحقيق في الأساليب القائمة على التدرج والقائم على الاضطرابات عن طريق قياس أدائها وجهود حسابية مطلوبة.في جميع تجاربنا، لاحظنا أن استخدام درجات الكلمة المطلقة يعزز أداء المشرفين المستند إلى التدرج بشكل كبير.علاوة على ذلك، نجمع بين طرق الشرح لفرق استغلال نقاط القوة في الأشرار الفردية للحصول على تفسيرات أفضل.نقترح استخدام الأساليب القائمة على التدرج المطلق.هذه العمل بشكل جيد
Current research on quality estimation of machine translation focuses on the sentence-level quality of the translations. By using explainability methods, we can use these quality estimations for word-level error identification. In this work, we compare different explainability techniques and investigate gradient-based and perturbation-based methods by measuring their performance and required computational efforts. Throughout our experiments, we observed that using absolute word scores boosts the performance of gradient-based explainers significantly. Further, we combine explainability methods to ensembles to exploit the strengths of individual explainers to get better explanations. We propose the usage of absolute gradient-based methods. These work comparably well to popular perturbation-based ones while being more time-efficient.
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