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Register a new userIITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary
IITP-MT في Wat2021: الترجمة الآلية العصبية متعددة اللغات Water-English باستخدام المفردات اللغوية
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تصف هذه الورقة أن الأنظمة المقدمة إلى المهمة المشتركة Wat 2021 Multiindicmt بواسطة فريق IITP-MT.نحن نقدم اثنين من أنظمة الترجمة الآلية العصبية متعددة اللغات (NMT) (Inster-to-English والإنجليزية إلى MEDER).ننهي جميع بيانات MED وتخلق المفردات الفرعية التي يتم مشاركتها بين جميع لغات ISS.نحن نستخدم نهج الترجمة الخلفي لتوليد البيانات الاصطناعية التي يتم إلحاقها بالتوازي Corpus وتستخدم لتدريب نماذجنا.يتم تقييم النماذج باستخدام درجات Bleu و Libes و AMFM مع نموذج MEDER-To-To-English يحقق 40.08 Bleu للزوج الهندي والإنجليزي ونموذج اللغة الإنجليزية إلى MERS لتحقيق 34.48 بلو للزوج باللغة الإنجليزية الهندية.ومع ذلك، نلاحظ أن مفردات الكلمة الفرعية المشتركة لا تساعد النموذج الإنجليزي إلى التروس في وقت الجيل، مما أدى إلى إنتاج ترجمات ذات نوعية رديئة للتاميل والتيلجو وميلايالام إلى أزواج باللغة الإنجليزية مع درجة بلو 8.51 و 6.25 و 3.79على التوالى.
This paper describes the systems submitted to WAT 2021 MultiIndicMT shared task by IITP-MT team. We submit two multilingual Neural Machine Translation (NMT) systems (Indic-to-English and English-to-Indic). We romanize all Indic data and create subword vocabulary which is shared between all Indic languages. We use back-translation approach to generate synthetic data which is appended to parallel corpus and used to train our models. The models are evaluated using BLEU, RIBES and AMFM scores with Indic-to-English model achieving 40.08 BLEU for Hindi-English pair and English-to-Indic model achieving 34.48 BLEU for English-Hindi pair. However, we observe that the shared romanized subword vocabulary is not helping English-to-Indic model at the time of generation, leading it to produce poor quality translations for Tamil, Telugu and Malayalam to English pairs with BLEU score of 8.51, 6.25 and 3.79 respectively.
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