@inproceedings{lee-etal-2022-formnet,
    title = "{F}orm{N}et: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction",
    author = "Lee, Chen-Yu  and
      Li, Chun-Liang  and
      Dozat, Timothy  and
      Perot, Vincent  and
      Su, Guolong  and
      Hua, Nan  and
      Ainslie, Joshua  and
      Wang, Renshen  and
      Fujii, Yasuhisa  and
      Pfister, Tomas",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.260",
    doi = "10.18653/v1/2022.acl-long.260",
    pages = "3735--3754",
    abstract = "Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practice due to their variety of layout patterns. We propose FormNet, a structure-aware sequence model to mitigate the suboptimal serialization of forms. First, we design Rich Attention that leverages the spatial relationship between tokens in a form for more precise attention score calculation. Second, we construct Super-Tokens for each word by embedding representations from their neighboring tokens through graph convolutions. FormNet therefore explicitly recovers local syntactic information that may have been lost during serialization. In experiments, FormNet outperforms existing methods with a more compact model size and less pre-training data, establishing new state-of-the-art performance on CORD, FUNSD and Payment benchmarks.",
}