QueryForm: A Simple Zero-shot Form Entity Query Framework
Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister
Abstract
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes both the document schema and a specific entity type into a query, which is used to prompt a Transformer model to perform a single entity extraction task. Furthermore, we propose to leverage large-scale query-entity pairs generated from form-like webpages with weak HTML annotations to pre-train QueryForm. By unifying pre-training and fine-tuning into the same query-based framework, QueryForm enables models to learn from structured documents containing various entities and layouts, leading to better generalization to target document types without the need for target-specific training data. QueryForm sets new state-of-the-art average F1 score on both the XFUND (+4.6% 10.1%) and the Payment (+3.2% 9.5%) zero-shot benchmark, with a smaller model size and no additional image input.- Anthology ID:
- 2023.findings-acl.255
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4146–4159
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.255
- DOI:
- 10.18653/v1/2023.findings-acl.255
- Cite (ACL):
- Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, and Tomas Pfister. 2023. QueryForm: A Simple Zero-shot Form Entity Query Framework. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4146–4159, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- QueryForm: A Simple Zero-shot Form Entity Query Framework (Wang et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.255.pdf