@inproceedings{wang-etal-2023-queryform,
title = "{Q}uery{F}orm: A Simple Zero-shot Form Entity Query Framework",
author = "Wang, Zifeng and
Zhang, Zizhao and
Devlin, Jacob and
Lee, Chen-Yu and
Su, Guolong and
Zhang, Hao and
Dy, Jennifer and
Perot, Vincent and
Pfister, Tomas",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.255",
doi = "10.18653/v1/2023.findings-acl.255",
pages = "4146--4159",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T QueryForm: A Simple Zero-shot Form Entity Query Framework
%A Wang, Zifeng
%A Zhang, Zizhao
%A Devlin, Jacob
%A Lee, Chen-Yu
%A Su, Guolong
%A Zhang, Hao
%A Dy, Jennifer
%A Perot, Vincent
%A Pfister, Tomas
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-queryform
%X 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.
%R 10.18653/v1/2023.findings-acl.255
%U https://aclanthology.org/2023.findings-acl.255
%U https://doi.org/10.18653/v1/2023.findings-acl.255
%P 4146-4159
Markdown (Informal)
[QueryForm: A Simple Zero-shot Form Entity Query Framework](https://aclanthology.org/2023.findings-acl.255) (Wang et al., Findings 2023)
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.