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 100x more data. We open-source our pretrained models and code.

翻译：感性转移学习对计算机愿景产生了重大影响,但国家语言方案的现有方法仍需要从头开始根据具体任务进行修改和培训。 我们提出了通用语言模型微调(ULMFiT ), 这是一种有效的转移学习方法,可以适用于国家语言方案的任何任务,并引入了对语言模型微调至关重要的技术。 我们的方法大大优于六种文本分类任务的最新技术,将大多数数据集的错误减少18-24%。 此外,只有100个有标签的例子,它与100种以上数据从零到零的培训效果相匹配。 我们开发了我们预先培训的模型和代码。