3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding
Yihao Ding, Lorenzo Vaiani, Caren Han, Jean Lee, Paolo Garza, Josiah Poon, Luca Cagliero
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.- Anthology ID:
- 2024.findings-acl.903
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15233–15244
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-acl.903/
- DOI:
- 10.18653/v1/2024.findings-acl.903
- Cite (ACL):
- Yihao Ding, Lorenzo Vaiani, Caren Han, Jean Lee, Paolo Garza, Josiah Poon, and Luca Cagliero. 2024. 3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding. In Findings of the Association for Computational Linguistics: ACL 2024, pages 15233–15244, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- 3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding (Ding et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-acl.903.pdf