This tutorial demonstrates workflows to incorporate text data into actuarial
classification and regression tasks. The main focus is on methods employing
transformer-based models. A dataset of car accident descriptions with an
average length of 400 words, available in English and German, and a dataset
with short property insurance claims descriptions are used to demonstrate these
techniques. The case studies tackle challenges related to a multi-lingual
setting and long input sequences. They also show ways to interpret model
output, to assess and improve model performance, by fine-tuning the models to
the domain of application or to a specific prediction task. Finally, the
tutorial provides practical approaches to handle classification tasks in
situations with no or only few labeled data. The results achieved by using the
language-understanding skills of off-the-shelf natural language processing
(NLP) models with only minimal pre-processing and fine-tuning clearly
demonstrate the power of transfer learning for practical applications.

本教程介绍了将文本数据纳入精算分类和回归任务的工作流程，重点介绍了使用基于 transformer 模型的方法。通过对一组包含 400 个单词的汽车事故的描述和一组短期财产保险索赔描述的数据集进行演示，探讨了多语言环境和长输入序列等挑战性问题，同时展示了解释模型输出、评估和改进模型性能的方法，通过在应用领域或特定预测任务上对模型进行微调，提供了实现分类任务处理的实用方法。使用仅经过最少预处理和微调的现成自然语言处理 (NLP) 模型的语言理解技巧所实现的结果，充分展示了迁移学习在实际应用中的威力。