@inproceedings{ding-etal-2024-3mvrd,
title = "3{MVRD}: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding",
author = "Ding, Yihao and
Vaiani, Lorenzo and
Han, Caren and
Lee, Jean and
Garza, Paolo and
Poon, Josiah and
Cagliero, Luca",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.903",
pages = "15233--15244",
abstract = "This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained levels by facilitating a nuanced correlation between token and entity representations, addressing the complexities inherent in form documents. Additionally, we introduce new inter-grained and cross-grained loss functions to further refine diverse multi-teacher knowledge distillation transfer process, presenting distribution gaps and a harmonised understanding of form documents. Through a comprehensive evaluation across publicly available form document understanding datasets, our proposed model consistently outperforms existing baselines, showcasing its efficacy in handling the intricate structures and content of visually complex form documents.",
}
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%0 Conference Proceedings
%T 3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding
%A Ding, Yihao
%A Vaiani, Lorenzo
%A Han, Caren
%A Lee, Jean
%A Garza, Paolo
%A Poon, Josiah
%A Cagliero, Luca
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F ding-etal-2024-3mvrd
%X This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained levels by facilitating a nuanced correlation between token and entity representations, addressing the complexities inherent in form documents. Additionally, we introduce new inter-grained and cross-grained loss functions to further refine diverse multi-teacher knowledge distillation transfer process, presenting distribution gaps and a harmonised understanding of form documents. Through a comprehensive evaluation across publicly available form document understanding datasets, our proposed model consistently outperforms existing baselines, showcasing its efficacy in handling the intricate structures and content of visually complex form documents.
%U https://aclanthology.org/2024.findings-acl.903
%P 15233-15244
Markdown (Informal)
[3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding](https://aclanthology.org/2024.findings-acl.903) (Ding et al., Findings 2024)
ACL