This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task.
翻译:本文介绍了我们竞争进入MEDIQA-2019竞赛的系统,我们使用多种来源转让学习方法将MT-DNN和SciBERT的知识转让给医疗领域的自然语言理解任务,为转移学习微调,我们利用关于国家语言倡议、RQE和QA的一般和医疗领域的多任务学习来提高绩效,拟议的方法证明对医疗领域的自然语言理解有效,我们在质量评估任务中排第一位。