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。

使用 Transformers 的自然语言处理在保险精算中的应用：在文本特征中的案例研究

Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial Context

Phoneme boundary detection plays an essential first step for a variety of
speech processing applications such as speaker diarization, speech science,
keyword spotting, etc. In this work, we propose a neural architecture coupled
with a parameterized structured loss function to learn segmental
representations for the task of phoneme boundary detection. First, we evaluated
our model when the spoken phonemes were not given as input. Results on the
TIMIT and Buckeye corpora suggest that the proposed model is superior to the
baseline models and reaches state-of-the-art performance in terms of F1 and
R-value. We further explore the use of phonetic transcription as additional
supervision and show this yields minor improvements in performance but
substantially better convergence rates. We additionally evaluate the model on a
Hebrew corpus and demonstrate such phonetic supervision can be beneficial in a
multi-lingual setting.

本文探究了使用神经网络和参数化结构化损失函数进行音素边界检测的方法，通过实验表明该方法的性能优于基准模型，使用音素翻译可以稍微提高性能并显著提高收敛速度，并在希伯来语语料库中进行了进一步的评估，表明在多语言环境下使用音素翻译可以获得更好的效果。