  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.
