Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER
Jun Zhao, Xin Zhao, WenYu Zhan, Tao Gui, Qi Zhang, Liang Qiao, Zhanzhan Cheng, Shiliang Pu
Abstract
The introduction of multimodal information and pretraining technique significantly improves entity recognition from visually-rich documents. However, most of the existing methods pay unnecessary attention to irrelevant regions of the current document while ignoring the potentially valuable information in related documents. To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document. 2) To further enrich the entity-related context, we propose a cross-document information awareness technique, which enables the model to collect more evidence across documents to assist in prediction. The experimental results on two documents understanding benchmarks covering eight languages demonstrate that our method outperforms the SOTA methods.- Anthology ID:
- 2022.coling-1.177
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2034–2043
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.177
- DOI:
- Bibkey:
- Cite (ACL):
- Jun Zhao, Xin Zhao, WenYu Zhan, Tao Gui, Qi Zhang, Liang Qiao, Zhanzhan Cheng, and Shiliang Pu. 2022. Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2034–2043, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (Zhao et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.177.pdf
- Data
- FUNSD
Export citation
@inproceedings{zhao-etal-2022-read-extensively, title = "Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents {NER}", author = "Zhao, Jun and Zhao, Xin and Zhan, WenYu and Gui, Tao and Zhang, Qi and Qiao, Liang and Cheng, Zhanzhan and Pu, Shiliang", editor = "Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.177", pages = "2034--2043", abstract = "The introduction of multimodal information and pretraining technique significantly improves entity recognition from visually-rich documents. However, most of the existing methods pay unnecessary attention to irrelevant regions of the current document while ignoring the potentially valuable information in related documents. To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document. 2) To further enrich the entity-related context, we propose a cross-document information awareness technique, which enables the model to collect more evidence across documents to assist in prediction. The experimental results on two documents understanding benchmarks covering eight languages demonstrate that our method outperforms the SOTA methods.", }
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%0 Conference Proceedings %T Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER %A Zhao, Jun %A Zhao, Xin %A Zhan, WenYu %A Gui, Tao %A Zhang, Qi %A Qiao, Liang %A Cheng, Zhanzhan %A Pu, Shiliang %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F zhao-etal-2022-read-extensively %X The introduction of multimodal information and pretraining technique significantly improves entity recognition from visually-rich documents. However, most of the existing methods pay unnecessary attention to irrelevant regions of the current document while ignoring the potentially valuable information in related documents. To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document. 2) To further enrich the entity-related context, we propose a cross-document information awareness technique, which enables the model to collect more evidence across documents to assist in prediction. The experimental results on two documents understanding benchmarks covering eight languages demonstrate that our method outperforms the SOTA methods. %U https://aclanthology.org/2022.coling-1.177 %P 2034-2043
Markdown (Informal)
[Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER](https://aclanthology.org/2022.coling-1.177) (Zhao et al., COLING 2022)
- Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (Zhao et al., COLING 2022)
ACL
- Jun Zhao, Xin Zhao, WenYu Zhan, Tao Gui, Qi Zhang, Liang Qiao, Zhanzhan Cheng, and Shiliang Pu. 2022. Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2034–2043, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.