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 our transfer learning approach on 16 diverse NLP tasks, and find that it effectively combines various types of knowledge at different layers of the model. We show that our approach outperforms traditional strategies such as full fine-tuning of the model as well as multi-task learning .