Universal Language Model Fine-tuning for Text Classification

摘要

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only $100$ labeled examples, it matches the performance of training from scratch on $100\times$ more data. We open-source our pretrained models and code22 2 http://nlp.fast.ai/ulmfit..