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Register a new userA Comparison of Different NMT Approaches to Low-Resource Dutch-Albanian Machine Translation
مقارنة بين نهج NMT المختلفة إلى الترجمة ذات الموارد الهولندية-الألبانية المنخفضة
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يمكن فهم لغات الموارد المنخفضة كنغات أكثر شحيحة، وأقل دراستها، أقل امتيازا، أقل شيوعا، والتي تكون أقل شيوعا والتي توجد فيها موارد أقل (Singh، 2008؛ Cieri et al.، 2016؛ Magueresse et al.، 2020) وبعد يركز البحث والتكنولوجيا لمعالجة اللغة الطبيعية (NLP) بشكل أساسي على تلك اللغات التي توجد بها مجموعات بيانات كبيرة متاحة. لتوضيح الاختلافات في توافر البيانات: هناك 6 ملايين مقالة في ويكيبيديا المتاحة للغة الإنجليزية، 2 مليون للهولندية، ومجرد 82 ألف للألبانية. تصبح قضية البيانات الشحيحة واضحة بشكل متزايد عندما تكون مجموعات البيانات المتوازية الكبيرة مطلوبة للتطبيقات مثل الترجمة الآلية العصبية (NMT). في هذا العمل، يمكننا التحقيق في أي مدى من الممكن الترجمة بين الألبانية (SQ) والهولندية (NL) مقارنة نموذج واحد إلى واحد (SQ↔AL)، نهج يستند إلى موارد منخفضة الموارد (الإنجليزية (EN) Pivot) والترجمة الصفرية بالرصاص (ZST) (جونسون وآخرون، 2016؛ نظام ماتوني وآخرون.، 2017). من تجاربنا، فإنه ينتج عن تفوق نموذج EN-PIVOT على حد سواء من طراز Zst المباشر. منذ غالبا ما تكون كميات صغيرة من البيانات الموازية متاحة لغات الموارد المنخفضة أو الإعدادات المنخفضة، أجريت التجارب باستخدام مجموعات صغيرة من بيانات NL↔SQ الموازية. بدا أن Zst هو أسوأ نماذج أداء. حتى عندما تمت إضافة البيانات الموازية المتاحة (nl↔sq)، أي في إعداد قليل من اللقطة (FST)، ظلت أسوأ نظام أداء وفقا ل Automatic (Bleu and Ter) والتقييم البشري.
Low-resource languages can be understood as languages that are more scarce, less studied, less privileged, less commonly taught and for which there are less resources available (Singh, 2008; Cieri et al., 2016; Magueresse et al., 2020). Natural Language Processing (NLP) research and technology mainly focuses on those languages for which there are large data sets available. To illustrate differences in data availability: there are 6 million Wikipedia articles available for English, 2 million for Dutch, and merely 82 thousand for Albanian. The scarce data issue becomes increasingly apparent when large parallel data sets are required for applications such as Neural Machine Translation (NMT). In this work, we investigate to what extent translation between Albanian (SQ) and Dutch (NL) is possible comparing a one-to-one (SQ↔AL) model, a low-resource pivot-based approach (English (EN) as pivot) and a zero-shot translation (ZST) (Johnson et al., 2016; Mattoni et al., 2017) system. From our experiments, it results that the EN-pivot-model outperforms both the direct one-to-one and the ZST model. Since often, small amounts of parallel data are available for low-resource languages or settings, experiments were conducted using small sets of parallel NL↔SQ data. The ZST appeared to be the worst performing models. Even when the available parallel data (NL↔SQ) was added, i.e. in a few-shot setting (FST), it remained the worst performing system according to the automatic (BLEU and TER) and human evaluation.
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