Before revision:  We empirically evaluate our transfer learning approach on 16 diverse NLP tasks, and show that it outperforms traditional strategies such as full fine-tuning of the model as well as multi-task learning  .
After revision:  We empirically evaluate AdapterFusion on 16 diverse NLP tasks, and show that it outperforms traditional strategies such as full fine-tuning of the model as well as multi-task learning .