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Register a new userMultilingual ELMo and the Effects of Corpus Sampling
إلمو متعدد اللغات وتأثيرات أخذ العينات Corpus
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تكتسب نماذج اللغة المحددة مسبقا بسرعة شعبية بسرعة في أنظمة NLP للغات غير الإنجليزية.تتميز معظم هذه النماذج بخطوة أخذ عينات مهمة مهمة في عملية تتراكم بيانات التدريب بلغات مختلفة، للتأكد من أن الإشارة من لغات الموارد الأفضل لا تغرق منها أكثر الموارد.في هذه الدراسة، ندرب العديد من النماذج اللغوية المتكررة متعددة اللغات، بناء على بنية ELMO، وتحليل تأثير نسب حجم Corpus المتغير على الأداء المصب، بالإضافة إلى اختلاف الأداء بين نماذج أحادية الألوان لكل لغة، ونماذج لغة متعددة اللغات الأوسعوبعدكجزء من هذا الجهد، نجعل هذه النماذج المدربة المتاحة للاستخدام العام.
Multilingual pretrained language models are rapidly gaining popularity in NLP systems for non-English languages. Most of these models feature an important corpus sampling step in the process of accumulating training data in different languages, to ensure that the signal from better resourced languages does not drown out poorly resourced ones. In this study, we train multiple multilingual recurrent language models, based on the ELMo architecture, and analyse both the effect of varying corpus size ratios on downstream performance, as well as the performance difference between monolingual models for each language, and broader multilingual language models. As part of this effort, we also make these trained models available for public use.
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