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Register a new userCrosslingual Embeddings are Essential in UNMT for distant languages: An English to IndoAryan Case Study
تعد Erblingual Embeddings ضرورية في UNMT للغات البعيدة: دراسة حالة الإنجليزية إلى Indooaryan
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تقدم التطورات الحديثة في الترجمة الآلية العصبية غير المدعومة (IPNMT) من الفجوة بين أداء ترجمة الآلات الخاضعة للإشراف وغير المعروضة لأزواج اللغة ذات الصلة عن كثب. ومع ذلك، والوضع مختلف جدا على أزواج اللغة البعيدة. يؤدي نقص التداخل في المعجم وانخفاض التشابه النحوي، مثل اللغة الإنجليزية واللغات الهندية إلى ضعف جودة الترجمة في أنظمة IPS الحالية. في هذه الورقة، نعرض أن تهيئة طبقة التضمين من طرازات التضمين من طرازات بروتوكول الكثال الكثال الكربون البرمجية مع ادبات متبلة يؤدي إلى تحسينات نقاط بلو كبيرة على نماذج IPS الحالية حيث تتم تهيئة أوزان طبقة تضمينها بشكل عشوائي. مما يؤدي وتجميد الأوزان طبقة التضمين إلى تحسين مكاسب أفضل مقارنة بتحديث أوزان طبقة تضمينها أثناء التدريب. لقد جربنا باستخدام تسلسل ملثمين للتسلسل (الكتلة) وتدينك مناهج AUTONCONDER (DAE) لنهج البث لمدة ثلاث أزواج لغة بعيدة. تهيئة تضمين التضمين المتبادلة المقترحة تحسن نتيجة بلو ما يصل إلى عشر مرات فوق خط الأساس للإنجليزية-الهندية والإنجليزية-البنغالية والإنجليزية-الغوجاراتية. يوضح تحليلنا أن تهيئة طبقة التضمين مع رسم خرائط تضمين التضمين الساكنة ضرورية لتدريب نماذج بعثة الأمم المتحدة في غول الرصاص على أزواج اللغة البعيدة.
Recent advances in Unsupervised Neural Machine Translation (UNMT) has minimized the gap between supervised and unsupervised machine translation performance for closely related language-pairs. However and the situation is very different for distant language pairs. Lack of overlap in lexicon and low syntactic similarity such as between English and IndoAryan languages leads to poor translation quality in existing UNMT systems. In this paper and we show that initialising the embedding layer of UNMT models with cross-lingual embeddings leads to significant BLEU score improvements over existing UNMT models where the embedding layer weights are randomly initialized. Further and freezing the embedding layer weights leads to better gains compared to updating the embedding layer weights during training. We experimented using Masked Sequence to Sequence (MASS) and Denoising Autoencoder (DAE) UNMT approaches for three distant language pairs. The proposed cross-lingual embedding initialization yields BLEU score improvement of as much as ten times over the baseline for English-Hindi and English-Bengali and English-Gujarati. Our analysis shows that initialising embedding layer with static cross-lingual embedding mapping is essential for training of UNMT models for distant language-pairs.
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