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Register a new userBeyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation
ما وراء ميزات مربع الزجاج: تقدير جودة عدم اليقين المعزز للترجمة الآلية العصبية
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يلعب تقدير الجودة (QE) دورا أساسيا في تطبيقات الترجمة الآلية (MT).تقليديا، يقبل نظام QE النصي المصدر الأصلي والترجمة من نظام MT مربع أسود كإدخال.في الآونة الأخيرة، تشير بعض الدراسات إلى أنه كمنتج ثانوي للترجمة، يستفيد QE من نموذج معلومات بيانات النموذج والتدريب من نظام MT حيث تأتي الترجمات، وتسمى QE الزجاجي ".في هذه الورقة، نقوم بتوسيع تعريف صندوق الزجاج QE "بشكل عام إلى كمية عدم اليقين مع حدود عدم اليقين مع كل من الأساليب السوداء والزجاج" مناهضات "وتصميم العديد من الميزات التي استنتجتها منهم لتخفيف تجربة جديدة في تحسين أداء QE.نقترح إطارا لفوست هندسة الميزة لتقدير عدم اليقين في نموذج لغة متمربا مسبقا مسبقا للتنبؤ بجودة الترجمة.تظهر نتائج التجربة أن طريقتنا تحقق أدائها الحديثة في مجموعات البيانات ذات المهمة المشتركة مع WMT 2020 QE.
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a few studies indicate that as a by-product of translation, QE benefits from the model and training data's information of the MT system where the translations come from, and it is called the glass-box QE''. In this paper, we extend the definition of glass-box QE'' generally to uncertainty quantification with both black-box'' and glass-box'' approaches and design several features deduced from them to blaze a new trial in improving QE's performance. We propose a framework to fuse the feature engineering of uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.
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Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT. Training QE models require massive parallel data with hand-crafted q
uality annotations, which are time-consuming and labor-intensive to obtain. To address the issue of the absence of annotated training data, previous studies attempt to develop unsupervised QE methods. However, very few of them can be applied to both sentence- and word-level QE tasks, and they may suffer from noises in the synthetic data. To reduce the negative impact of noises, we propose a self-supervised method for both sentence- and word-level QE, which performs quality estimation by recovering the masked target words. Experimental results show that our method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.
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