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Register a new userMultilingual Machine Translation Systems from Microsoft for WMT21 Shared Task
أنظمة ترجمة الآلة متعددة اللغات من Microsoft for WMT21 المهمة المشتركة
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يصف هذا التقرير أن أنظمة ترجمة آلات Microsoft للمهمة المشتركة WMT21 على الترجمة ذات الجهاز متعدد اللغات على نطاق واسع.شاركنا في مسارات التقييم الثلاثة بما في ذلك المسار الكبير والمسارين الصغيرين حيث لا يتم حدوث المرء السابق وأن الأخيران مقيدان تماما.تم تهيئة الطلبات النموذجية الخاصة بنا إلى المهمة المشتركة مع Deltalm، وهو نموذج فك ترميز ترميز متعدد اللغز متعدد اللغات مسبقا، ويتم ضبطه بشكل جيد في المقابل مع البيانات الموازية المستديرة ومصادر البيانات المسموح بها وفقا لإعدادات المسار، جنبا إلى جنب مع تطبيق التعلم التدريجي والتكرارمناهج الترجمة الخلفية لمزيد من تحسين الأداء.تم تصنيف التقديمات النهائية لدينا في المرتبة الأولى على ثلاثة مسارات من حيث مقياس التقييم التلقائي.
This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. We participated in all three evaluation tracks including Large Track and two Small Tracks where the former one is unconstrained and the latter two are fully constrained. Our model submissions to the shared task were initialized with DeltaLM, a generic pre-trained multilingual encoder-decoder model, and fine-tuned correspondingly with the vast collected parallel data and allowed data sources according to track settings, together with applying progressive learning and iterative back-translation approaches to further improve the performance. Our final submissions ranked first on three tracks in terms of the automatic evaluation metric.
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This paper describes Charles University sub-mission for Terminology translation Shared Task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high over
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This paper describes Charles University sub-mission for Terminology translation shared task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high over
all translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database.
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In this paper, we describe our end-to-end multilingual speech translation system submitted to the IWSLT 2021 evaluation campaign on the Multilingual Speech Translation shared task. Our system is built by leveraging transfer learning across modalities
, tasks and languages. First, we leverage general-purpose multilingual modules pretrained with large amounts of unlabelled and labelled data. We further enable knowledge transfer from the text task to the speech task by training two tasks jointly. Finally, our multilingual model is finetuned on speech translation task-specific data to achieve the best translation results. Experimental results show our system outperforms the reported systems, including both end-to-end and cascaded based approaches, by a large margin. In some translation directions, our speech translation results evaluated on the public Multilingual TEDx test set are even comparable with the ones from a strong text-to-text translation system, which uses the oracle speech transcripts as input.
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