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Register a new userIs BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models' Transferability
هل بيرت لمعرفة المعرفة متعددة التخصصات؟اكتشاف مفاجئ من تحويل النماذج المدربة مسبقا
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تحقق هذه الورقة فيما إذا كانت قوة النماذج المدربة مسبقا على البيانات النصية، مثل Bert، يمكن نقلها إلى تطبيقات تصنيف تسلسل الرمز المميز.للتحقق من قابلية نقل النماذج المدربة مسبقا، نقوم باختبار النماذج المدربة مسبقا على مهام تصنيف النص مع معاني عدم تطابق الرموز، وبيانات تصنيف تسلسل التسلسل غير المدرسي في العالم الحقيقي، بما في ذلك الحمض الأميني والحمض النووي والموسيقى.نجد أنه حتى على البيانات غير النصية، تتخطى النماذج المدربة مسبقا على النص بشكل أسرع، وأداء أفضل من النماذج ذات الادعاء بشكل عشوائي، وأسوأ قليلا فقط من النماذج باستخدام المعرفة الخاصة بمهام المهام.نجد أيضا أن تمثيل النماذج المدربة مسبقا للنصوص وغير النصية تشترك في أوجه التشابه غير التافهة.
This paper investigates whether the power of the models pre-trained on text data, such as BERT, can be transferred to general token sequence classification applications. To verify pre-trained models' transferability, we test the pre-trained models on text classification tasks with meanings of tokens mismatches, and real-world non-text token sequence classification data, including amino acid, DNA, and music. We find that even on non-text data, the models pre-trained on text converge faster, perform better than the randomly initialized models, and only slightly worse than the models using task-specific knowledge. We also find that the representations of the text and non-text pre-trained models share non-trivial similarities.
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Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results. However, the fine-tuning process inevitably changes the parameters of the pre-trained model and weakens its cro
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Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models. The essential idea is to exit early without passing through all the inference layers at the inference stage. To make accurate predictions for down
stream tasks, the hierarchical linguistic information embedded in all layers should be jointly considered. However, much of the research up to now has been limited to use local representations of the exit layer. Such treatment inevitably loses information of the unused past layers as well as the high-level features embedded in future layers, leading to sub-optimal performance. To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective. We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions. Extensive experiments demonstrate that our method outperforms previous early exit methods by a large margin, yielding better and robust performance.
We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform
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