@inproceedings{cheng-etal-2025-xformparser,
title = "{XF}orm{P}arser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser",
author = "Cheng, Xianfu and
Zhang, Hang and
Yang, Jian and
Li, Xiang and
Zhou, Weixiao and
Liu, Fei and
Wu, Kui and
Guan, Xiangyuan and
Sun, Tao and
Wu, Xianjie and
Li, Tongliang and
Li, Zhoujun",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.41/",
pages = "606--620",
abstract = "In the domain of Document AI, parsing semi-structured image form is a crucial Key Information Extraction (KIE) task. The advent of pre-trained multimodal models significantly empowers Document AI frameworks to extract key information from form documents in different formats such as PDF, Word, and images. Nonetheless, form parsing is still encumbered by notable challenges like subpar capabilities in multilingual parsing and diminished recall in industrial contexts in rich text and rich visuals. In this work, we introduce a simple but effective Multimodal and Multilingual semi-structured FORM PARSER (XFormParser), which is anchored on a comprehensive Transformer-based pre-trained language model and innovatively amalgamates semantic entity recognition (SER) and relation extraction (RE) into a unified framework. Combined with Bi-LSTM, the performance of multilingual parsing is significantly improved. Furthermore, we develop InDFormSFT, a pioneering supervised fine-tuning (SFT) industrial dataset that specifically addresses the parsing needs of forms in a variety of industrial contexts. Through rigorous testing on established benchmarks, XFormParser has demonstrated its unparalleled effectiveness and robustness. Compared to existing state-of-the-art (SOTA) models, XFormParser notably achieves up to 1.79{\%} F1 score improvement on RE tasks in language-specific settings. It also exhibits exceptional improvements in cross-task performance in both multilingual and zero-shot settings."
}
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<abstract>In the domain of Document AI, parsing semi-structured image form is a crucial Key Information Extraction (KIE) task. The advent of pre-trained multimodal models significantly empowers Document AI frameworks to extract key information from form documents in different formats such as PDF, Word, and images. Nonetheless, form parsing is still encumbered by notable challenges like subpar capabilities in multilingual parsing and diminished recall in industrial contexts in rich text and rich visuals. In this work, we introduce a simple but effective Multimodal and Multilingual semi-structured FORM PARSER (XFormParser), which is anchored on a comprehensive Transformer-based pre-trained language model and innovatively amalgamates semantic entity recognition (SER) and relation extraction (RE) into a unified framework. Combined with Bi-LSTM, the performance of multilingual parsing is significantly improved. Furthermore, we develop InDFormSFT, a pioneering supervised fine-tuning (SFT) industrial dataset that specifically addresses the parsing needs of forms in a variety of industrial contexts. Through rigorous testing on established benchmarks, XFormParser has demonstrated its unparalleled effectiveness and robustness. Compared to existing state-of-the-art (SOTA) models, XFormParser notably achieves up to 1.79% F1 score improvement on RE tasks in language-specific settings. It also exhibits exceptional improvements in cross-task performance in both multilingual and zero-shot settings.</abstract>
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%0 Conference Proceedings
%T XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser
%A Cheng, Xianfu
%A Zhang, Hang
%A Yang, Jian
%A Li, Xiang
%A Zhou, Weixiao
%A Liu, Fei
%A Wu, Kui
%A Guan, Xiangyuan
%A Sun, Tao
%A Wu, Xianjie
%A Li, Tongliang
%A Li, Zhoujun
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F cheng-etal-2025-xformparser
%X In the domain of Document AI, parsing semi-structured image form is a crucial Key Information Extraction (KIE) task. The advent of pre-trained multimodal models significantly empowers Document AI frameworks to extract key information from form documents in different formats such as PDF, Word, and images. Nonetheless, form parsing is still encumbered by notable challenges like subpar capabilities in multilingual parsing and diminished recall in industrial contexts in rich text and rich visuals. In this work, we introduce a simple but effective Multimodal and Multilingual semi-structured FORM PARSER (XFormParser), which is anchored on a comprehensive Transformer-based pre-trained language model and innovatively amalgamates semantic entity recognition (SER) and relation extraction (RE) into a unified framework. Combined with Bi-LSTM, the performance of multilingual parsing is significantly improved. Furthermore, we develop InDFormSFT, a pioneering supervised fine-tuning (SFT) industrial dataset that specifically addresses the parsing needs of forms in a variety of industrial contexts. Through rigorous testing on established benchmarks, XFormParser has demonstrated its unparalleled effectiveness and robustness. Compared to existing state-of-the-art (SOTA) models, XFormParser notably achieves up to 1.79% F1 score improvement on RE tasks in language-specific settings. It also exhibits exceptional improvements in cross-task performance in both multilingual and zero-shot settings.
%U https://aclanthology.org/2025.coling-main.41/
%P 606-620
Markdown (Informal)
[XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://aclanthology.org/2025.coling-main.41/) (Cheng et al., COLING 2025)
ACL
- Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, and Zhoujun Li. 2025. XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser. In Proceedings of the 31st International Conference on Computational Linguistics, pages 606–620, Abu Dhabi, UAE. Association for Computational Linguistics.