用保险文档从头训练LayoutLM，提升命名实体识别效果。
Training LayoutLM from Scratch for Efficient Named-Entity Recognition in the Insurance Domain
- 基于保险薪资单数据集，从头训练LayoutLM以适配领域需求。
- 在匿名保险文件上实现与主流模型相当的实体识别准确率。
- 小模型也能达到高效性能，适合资源受限场景使用。
通用预训练神经网络在金融、保险等专业领域表现不佳，主要因训练数据与下游任务存在领域差异，且受隐私限制，领域内数据往往稀缺。本文对比了LayoutLM的不同预训练策略，发现使用相关领域文档能显著提升命名实体识别（NER）性能。实验基于一个新型匿名保险类财务文档数据集Payslips，结果表明，仅用更小、更快的模型，也能获得具有竞争力的识别效果。
原文摘要 · Abstract (English)
Generic pre-trained neural networks may struggle to produce good results in specialized domains like finance and insurance. This is due to a domain mismatch between training data and downstream tasks, as in-domain data are often scarce due to privacy constraints. In this work, we compare different pre-training strategies for LayoutLM. We show that using domain-relevant documents improves results on a named-entity recognition (NER) problem using a novel dataset of anonymized insurance-related financial documents called Payslips. Moreover, we show that we can achieve competitive results using a smaller and faster model.
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