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Register a new userOn User Interfaces for Large-Scale Document-Level Human Evaluation of Machine Translation Outputs
على واجهات المستخدم للتقييم البشري على مستوى المستند على نطاق واسع لنواتج الترجمة الآلية
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تؤكد الدراسات الحديثة على حاجة إلى سياق وثائق في التقييم البشري لترجمات الماكينة، لكن القليل من الأبحاث قد تم في تأثير واجهات المستخدم على الإنتاجية العنصرية وموثوقية التقييمات.في هذا العمل، نقوم بمقارنة بيانات التقييم البشري من أحدث حملتين تقييمين من WMT التي تم جمعها عبر طريقتين مختلفتين لتقييم مستوى المستندات.يوضح تحليلنا أن اتباع نهج تركز على المستندات في التقييم حيث يتم عرض العنصي مع سياق المستند بأكمله على الشاشة يؤدي إلى تقييمات أعلى جودة ومستوى المستندات.إنه يحسن الارتباط بين القطاع وعشرات المستندات ويزيد من اتفاقية المشتركة بين النقاط عن درجات الوثائق ولكنها أكثر بكثير من الوقت المستهلكة للمعجبين.
Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator productivity and the reliability of assessments. In this work, we compare human assessment data from the last two WMT evaluation campaigns collected via two different methods for document-level evaluation. Our analysis shows that a document-centric approach to evaluation where the annotator is presented with the entire document context on a screen leads to higher quality segment and document level assessments. It improves the correlation between segment and document scores and increases inter-annotator agreement for document scores but is considerably more time consuming for annotators.
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This paper illustrates our approach to the shared task on large-scale multilingual machine translation in the sixth conference on machine translation (WMT-21). In this work, we aim to build a single multilingual translation system with a hypothesis t
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This work investigates neural machine translation (NMT) systems for translating English user reviews into Croatian and Serbian, two similar morphologically complex languages. Two types of reviews are used for testing the systems: IMDb movie reviews a
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