In the domain of document AI, semi-structured form parsing plays a crucial
role. This task leverages techniques from key information extraction (KIE),
dealing with inputs that range from plain text to intricate modal data
comprising images and structural layouts. The advent of pre-trained multimodal
models has driven the extraction of key information from form documents in
different formats such as PDFs and images. Nonetheless, the endeavor of form
parsing is still encumbered by notable challenges like subpar capabilities in
multi-lingual parsing and diminished recall in contexts rich in text and
visuals. In this work, we introduce a simple but effective \textbf{M}ultimodal
and \textbf{M}ultilingual semi-structured \textbf{FORM} \textbf{PARSER}
(\textbf{XFormParser}), which is anchored on a comprehensive pre-trained
language model and innovatively amalgamates semantic entity recognition (SER)
and relation extraction (RE) into a unified framework, enhanced by a novel
staged warm-up training approach that employs soft labels to significantly
refine form parsing accuracy without amplifying inference overhead.
Furthermore, we have developed a groundbreaking benchmark dataset, named
InDFormBench, catering specifically to the parsing requirements of multilingual
forms in various industrial contexts. Through rigorous testing on established
multilingual benchmarks and InDFormBench, XFormParser has demonstrated its
unparalleled efficacy, notably surpassing the state-of-the-art (SOTA) models in
RE tasks within language-specific setups by achieving an F1 score improvement
of up to 1.79\%. Our framework exhibits exceptionally improved performance
across tasks in both multi-language and zero-shot contexts when compared to
existing SOTA benchmarks. The code is publicly available at
this https URL

在文档 AI 领域中，半结构化表单解析起着至关重要的作用。本研究提出了一种简单而有效的多模态和多语言半结构化表单解析器（XFormParser），它基于全面的预训练语言模型，并创新地融合了语义实体识别（SER）和关系抽取（RE）技术。通过针对多语言表单解析需求开发了一个创新性的基准数据集，通过在多语言基准和新开发的数据集上进行严格测试，XFormParser 在语言特定设置中的 RE 任务中取得了卓越的效果，F1 得分提高了高达 1.79％。与现有的 SOTA 基准相比，我们的框架在多语言和零样本环境中的性能显著提高。