论文标题
使用变形金刚的天然语言处理的精算应用：在精算环境中使用文本功能的案例研究
Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial Context
论文作者
论文摘要
本教程展示了工作流程，将文本数据纳入精算分类和回归任务。主要重点是采用基于变压器模型的方法。平均长度为400个单词的车祸描述的数据集，英语和德语可用，以及具有简短财产保险索赔的数据集用来证明这些技术。案例研究应对与多语言环境和长输入序列有关的挑战。他们还展示了通过将模型调整到应用程序领域或特定预测任务的模型来解释模型输出，评估和改善模型性能的方法。最后，该教程提供了在没有或仅有的标签数据（包括但不限于Chatgpt）的情况下处理分类任务的实用方法。通过使用最小的预处理和微调的现成自然语言处理（NLP）模型的语言理解技能（NLP）模型实现的结果清楚地证明了用于实际应用的转移学习能力。
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, including but not limited to ChatGPT. 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.
