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Register a new userBME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection
التقديم BME ل SIGMORPHON 2021 المهمة المشتركة 0. نهج تدريب ثلاثي خطوات مع تكبير البيانات للانضباط المورفولوجي
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نقدم تقديم BME لمهمة Sigmorphon 2021 0 الجزء 1، التعميم عبر المهمة المشتركة بين اللغات المتنوعة من الناحية النموذجية.نحن نستخدم نموذج فك تشفير LSTM مع ثلاثة خطوات التدريب المدرب لأول مرة على جميع اللغات، ثم ضبطها على كل عائلة لغة وأخيرا ضبطها على اللغات الفردية.نستخدم نوع مختلف من تقنية تكبير البيانات في الخطوتين الأولين.تفوق نظامنا على التقديم الآخر الوحيد.على الرغم من أنه لا يزال أسوأ من خط الأساس المحول الذي أصدره المنظمون، فإن نموذجنا أبسط وأن تقنياتنا تكبير البيانات تنطبق بسهولة على لغات جديدة.نقوم بإجراء دراسات الاجتثاث وإظهار أن تقنيات المعزز والخطوات التدريبية الثلاثة غالبا ما تساعد ولكن في بعض الأحيان يكون لها تأثير سلبي.رمز لدينا هو متاح علنا.
We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task. We use an LSTM encoder-decoder model with three step training that is first trained on all languages, then fine-tuned on each language family and finally fine-tuned on individual languages. We use a different type of data augmentation technique in the first two steps. Our system outperformed the only other submission. Although it remains worse than the Transformer baseline released by the organizers, our model is simpler and our data augmentation techniques are easily applicable to new languages. We perform ablation studies and show that the augmentation techniques and the three training steps often help but sometimes have a negative effect. Our code is publicly available.
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