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Register a new userResults of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain
نتائج مقاييس WMT21 المهمة المشتركة: تقييم المقاييس مع التقييمات البشرية القائمة على الخبراء على نطاق TED و TEWS
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تقدم هذه الورقة نتائج المهمة المشتركة للمقاييس WMT21. طلب من المشاركين تسجيل مخرجات أنظمة الترجمة المتنافسة في مهمة الترجمة الأخبار WMT21 مع مقاييس أوتوماتيكية على مجطتين مختلفتين: أخبار ومحادثات تيد. تم تقييم جميع المقاييس على مدى ارتباطها على مستوى النظام والقطاع مع التصنيفات البشرية. على عكس إصدارات السنوات السابقة، فقد استحوذنا هذا العام على تصنيفاتنا الإنسانية الخاصة بنا على أساس التقييم البشري القائم على الخبراء عبر مقاييس الجودة متعددة الأبعاد (MQM). يحتوي هذا الإعداد على العديد من المزايا: (1) قد أظهر التقييم القائم على الخبراء أنه أكثر موثوقية، (2) تمكنا من تقييم جميع المقاييس على مجاليين مختلفين باستخدام ترجمات نفس أنظمة MT، (III) أضفنا 5 إضافية الترجمات القادمة من نفس النظام أثناء تطوير النظام. بالإضافة إلى ذلك، صممنا ثلاث مجموعات تحدي تقيم متانة جميع المقاييس التلقائية. نقدم تحليلا مكثفا حول مدى أداء المقاييس على أزواج ثلاث لغات: الإنجليزية إلى الألمانية والإنجليزية إلى الروسية والصينية إلى الإنجليزية. نوضح تأثير الترجمات المرجعية المختلفة على المقاييس المستندة إلى المرجع ومقارنة شرح MQM القائم على الخبراء مع درجات DA المكتسبة بواسطة WMT.
This paper presents the results of the WMT21 Metrics Shared Task. Participants were asked to score the outputs of the translation systems competing in the WMT21 News Translation Task with automatic metrics on two different domains: news and TED talks. All metrics were evaluated on how well they correlate at the system- and segment-level with human ratings. Contrary to previous years' editions, this year we acquired our own human ratings based on expert-based human evaluation via Multidimensional Quality Metrics (MQM). This setup had several advantages: (i) expert-based evaluation has been shown to be more reliable, (ii) we were able to evaluate all metrics on two different domains using translations of the same MT systems, (iii) we added 5 additional translations coming from the same system during system development. In addition, we designed three challenge sets that evaluate the robustness of all automatic metrics. We present an extensive analysis on how well metrics perform on three language pairs: English to German, English to Russian and Chinese to English. We further show the impact of different reference translations on reference-based metrics and compare our expert-based MQM annotation with the DA scores acquired by WMT.
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