%0 Generic %1 howard2018universal %A Howard, Jeremy %A Ruder, Sebastian %D 2018 %K dfg-antrag-steckbriefe nlp transfer transferlearning ulmfit %T Universal Language Model Fine-tuning for Text Classification %U http://arxiv.org/abs/1801.06146 %X 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.

@misc{howard2018universal, abstract = {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.}, added-at = {2019-09-10T23:02:14.000+0200}, author = {Howard, Jeremy and Ruder, Sebastian}, biburl = {https://www.bibsonomy.org/bibtex/2d7ba313d42b8da06500bef2945b7b968/albinzehe}, description = {Universal Language Model Fine-tuning for Text Classification}, interhash = {4a4b12e7b6b8f26c4fc6cdcbecfbbdc3}, intrahash = {d7ba313d42b8da06500bef2945b7b968}, keywords = {dfg-antrag-steckbriefe nlp transfer transferlearning ulmfit}, note = {cite arxiv:1801.06146Comment: ACL 2018, fixed denominator in Equation 3, line 3}, timestamp = {2021-01-12T14:02:20.000+0100}, title = {Universal Language Model Fine-tuning for Text Classification}, url = {http://arxiv.org/abs/1801.06146}, year = 2018 }