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Register a new userWhen Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
عندما تكون غير مرئية من MBERT هي مجرد بداية: معالجة لغات جديدة مع نماذج لغة متعددة اللغات
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أصبح التحويل التعلم بناء على نماذج لغة المحترفين على كمية كبيرة من البيانات الخام نموذجا جديدا للوصول إلى الأداء الحديث في NLP. ومع ذلك، لا يزال من غير الواضح كيف ينبغي تطبيق هذا النهج لغات غير مرئية غير مشمولة بأي نموذج لغوي متعدد اللغات واسعة ناتجا، والذي يتم توفير كمية صغيرة فقط من البيانات الخام فقط. في هذا العمل، من خلال مقارنة النماذج متعددة اللغات وأنتغوية، نوضح أن هذه النماذج تتصرف بطرق متعددة على اللغات غير المرئية. تستفيد بعض اللغات بشكل كبير من تعلم التعلم والتصرف بالمثل إلى لغات موارد عالية مرتبطة ارتباطا وثيقا في حين أن الآخرين على ما يبدو لا. التركيز على الأخير، نظرا لأن هذا الفشل في النقل يرتبط إلى حد كبير بتأثير البرنامج النصي المستخدم لكتابة هذه اللغات. نظهر أن ترجمة هذه اللغات تعمل بشكل كبير على تحسين إمكانات نماذج اللغة متعددة اللغات على نطاق واسع في مهام المصب. توفر هذه النتيجة اتجاها واعدا نحو جعل هذه النماذج متعددة اللغات بشكل كبير مفيدة لمجموعة جديدة من اللغات غير المرئية.
Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. We show that transliterating those languages significantly improves the potential of large-scale multilingual language models on downstream tasks. This result provides a promising direction towards making these massively multilingual models useful for a new set of unseen languages.
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Pre-trained multilingual language models have become an important building block in multilingual Natural Language Processing. In the present paper, we investigate a range of such models to find out how well they transfer discourse-level knowledge acr
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