يقدم هذا العمل مجموعة متنوعة بسيطة لتقييم جودة الترجمة الآلية بناء على مجموعة من الرواية ومقاييس ثابتة.نقيم الفرقة باستخدام ارتباط لعشرات MQM القائم على الخبراء ورشة عمل WMT 2021 المقاييس.في كل من إعدادات المونولينغوية والصفرية القصيرة، نعرض تحسنا كبيرا في الأداء على مقاييس واحدة.في الإعدادات المتبادلة، نوضح أيضا أن نهج الفرع ينطبق جيدا على اللغات غير المرئية.علاوة على ذلك، نحدد خط أساس قوي خال من المرجعية التي تتفوق باستمرار على تدابير بلو واستخدامها بشكل شائع وتحسين أداء فرقنا بشكل كبير.
This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics. We evaluate the ensemble using a correlation to expert-based MQM scores of the WMT 2021 Metrics workshop. In both monolingual and zero-shot cross-lingual settings, we show a significant performance improvement over single metrics. In the cross-lingual settings, we also demonstrate that an ensemble approach is well-applicable to unseen languages. Furthermore, we identify a strong reference-free baseline that consistently outperforms the commonly-used BLEU and METEOR measures and significantly improves our ensemble's performance.
References used
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