Extracting information from r\'esum\'es is typically formulated as a
two-stage problem, where the document is first segmented into sections and then
each section is processed individually to extract the target entities. Instead,
we cast the whole problem as sequence labeling in two levels -- lines and
tokens -- and study model architectures for solving both tasks simultaneously.
We build high-quality r\'esum\'e parsing corpora in English, French, Chinese,
Spanish, German, Portuguese, and Swedish. Based on these corpora, we present
experimental results that demonstrate the effectiveness of the proposed models
for the information extraction task, outperforming approaches introduced in
previous work. We conduct an ablation study of the proposed architectures. We
also analyze both model performance and resource efficiency, and describe the
trade-offs for model deployment in the context of a production environment.

提取信息从简历通常被制定为一个两阶段的问题，首先对文档进行分段，然后分别处理每个段落以提取目标实体。相反，我们将整个问题分为两个层次的序列标注 —— 行和标记，并研究用于同时解决两个任务的模型架构。我们建立了英文、法文、中文、西班牙文、德文、葡萄牙文和瑞典文的高质量简历解析语料库。基于这些语料库，我们提出了实验结果，证明了所提模型在信息提取任务中的有效性，优于之前工作中引入的方法。我们对提出的架构进行了消融研究。我们还分析了模型性能和资源效率，并描述了在生产环境中部署模型的权衡。