Universal Language Model Fine tuning for Text Classification
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.
ACL