A Progressive Learning Strategy for Medical Natural Language Understanding
Authors: ZHE YANG, Yi Huang, Mengfei Guo, Yaqin Chen, XiaotingWu, Junlan Feng, Chao Deng


Reviewer paper summaries: The paper proposes a method for multi-task learning that progressively trains the model, in which each task has access only to modules that follow in cascade in accordance with the difficulty of the tasks.
Summary of strengths: They proposed a novel model architecture based on a transformer with a clear definition and explanation.
The results of experiments demonstrate that it outperforms the existing multi-task learning methods, namely MT-DNN, MMoE, and Skill-net.
Summary of weaknesses: The model requires ranking the tasks according to their difficulty. However, the proposed approach seems ad hoc. In addition, the number of tasks is fixed to four. So, it remains questionable how the proposed method will be scaled in a general multi-task setting.
Comments, suggestions and typos: How was the model initialized? Was it trained from scratch or partially copied from the parameter of any existing transformer-based model?
I couldn't fully understand the diagram of Task Connection in the right part of the figure. Could you elaborate it?
The definition of Equation (7)  is the same as that of Equation (6); it needs to be checked.
Overall assessment scores: 3