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        <title>Graph Convolution for Multimodal Information Extraction from Visually Rich Documents</title>
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    <name type="personal">
        <namePart type="given">Xiaojing</namePart>
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    <name type="personal">
        <namePart type="given">Feiyu</namePart>
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    <name type="personal">
        <namePart type="given">Huasha</namePart>
        <namePart type="family">Zhao</namePart>
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            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)</title>
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            <namePart type="given">Anastassia</namePart>
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    <abstract>Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.</abstract>
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%0 Conference Proceedings
%T Graph Convolution for Multimodal Information Extraction from Visually Rich Documents
%A Liu, Xiaojing
%A Gao, Feiyu
%A Zhang, Qiong
%A Zhao, Huasha
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F liu-etal-2019-graph
%X Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.
%R 10.18653/v1/N19-2005
%U https://aclanthology.org/N19-2005
%U https://doi.org/10.18653/v1/N19-2005
%P 32-39

Markdown (Informal)

[Graph Convolution for Multimodal Information Extraction from Visually Rich Documents](https://aclanthology.org/N19-2005) (Liu et al., NAACL 2019)

• Graph Convolution for Multimodal Information Extraction from Visually Rich Documents (Liu et al., NAACL 2019)

ACL

• Xiaojing Liu, Feiyu Gao, Qiong Zhang, and Huasha Zhao. 2019. Graph Convolution for Multimodal Information Extraction from Visually Rich Documents. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 32–39, Minneapolis, Minnesota. Association for Computational Linguistics.