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Register a new userPractical Transformer-based Multilingual Text Classification
تصنيف النص متعدد اللغات المحول العملي
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الأساليب القائمة على المحولات جذابة لتصنيف النص متعدد اللغات، ولكن معايير البحوث الشائعة مثل XNLI (Conneau et al.، 2018) لا تعكس توافر البيانات ومجموعة واسعة من تطبيقات الصناعة.نقدم مقارنة تجريبية من نماذج تصنيف النص المستند إلى المحولات في مجموعة متنوعة من إعدادات الاحتياطية وغير اللغوية المتعددة اللغات والضبط.نقيم هذه الأساليب على مهمتين متميزتين في خمس لغات مختلفة.المغادرة من العمل السابق، تظهر نتائجنا أن نماذج لغة متعددة اللغات يمكن أن تتفوق على تلك المهام المطردة في بعض المهام المصب واللغات المستهدفة.نوضح بالإضافة إلى ذلك أن التعديلات العملية مثل المهام وعمالة العمل التكيفية والتكييف يمكن أن تحسن أداء التصنيف دون الحاجة إلى بيانات إضافية إضافية.
Transformer-based methods are appealing for multilingual text classification, but common research benchmarks like XNLI (Conneau et al., 2018) do not reflect the data availability and task variety of industry applications. We present an empirical comparison of transformer-based text classification models in a variety of practical monolingual and multilingual pretraining and fine-tuning settings. We evaluate these methods on two distinct tasks in five different languages. Departing from prior work, our results show that multilingual language models can outperform monolingual ones in some downstream tasks and target languages. We additionally show that practical modifications such as task- and domain-adaptive pretraining and data augmentation can improve classification performance without the need for additional labeled data.
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