We conduct investigations on clinical text machine translation by examining
multilingual neural network models using deep learning such as Transformer
based structures. Furthermore, to address the language resource imbalance
issue, we also carry out experiments using a transfer learning methodology
based on massive multilingual pre-trained language models (MMPLMs). The
experimental results on three subtasks including 1) clinical case (CC), 2)
clinical terminology (CT), and 3) ontological concept (OC) show that our models
achieved top-level performances in the ClinSpEn-2022 shared task on
English-Spanish clinical domain data. Furthermore, our expert-based human
evaluations demonstrate that the small-sized pre-trained language model (PLM)
won over the other two extra-large language models by a large margin, in the
clinical domain fine-tuning, which finding was never reported in the field.
Finally, the transfer learning method works well in our experimental setting
using the WMT21fb model to accommodate a new language space Spanish that was
not seen at the pre-training stage within WMT21fb itself, which deserves more
exploitation for clinical knowledge transformation, e.g. to investigate into
more languages. These research findings can shed some light on domain-specific
machine translation development, especially in clinical and healthcare fields.
Further research projects can be carried out based on our work to improve
healthcare text analytics and knowledge transformation.

通过研究多语言神经网络模型，使用深度学习，如基于 Transformer 的结构，我们在临床文本机器翻译方面进行了调查。此外，为了解决语言资源不平衡问题，我们还使用基于大规模多语言预训练语言模型（MMPLMs）的迁移学习方法进行了实验。在临床案例（CC）、临床术语（CT）和本体概念（OC）等三个子任务上的实验结果表明，我们的模型在 ClinSpEn-2022 共享任务中的英语 - 西班牙语临床领域数据中表现出了最佳水平。此外，基于专家评估的人工评估结果显示，小型预训练语言模型（PLM）在临床领域微调中以大幅度优于其他两个超大型语言模型，这是该领域前所未有的发现。最后，迁移学习方法在我们的实验设置中运行良好，使用 WMT21fb 模型适应了预训练阶段在 WMT21fb 中未见的西班牙语语言空间，这值得进一步探索临床知识转化方面，例如研究更多语言。这些研究结果可以为特定领域的机器翻译开发提供一些启示，尤其是在临床和医疗领域。在我们的工作基础上可以开展进一步的研究项目，以改进医疗文本分析和知识转化。