Papers by Cha Zhang
XDoc: Unified Pre-training for Cross-Format Document Understanding (2022.findings-emnlp)
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| Challenge: | Existing pre-trained models target one document format at a time, making it difficult to combine knowledge from multiple document formats. |
| Approach: | They propose a unified pre-trained model which deals with different document formats in a single model. |
| Outcome: | The proposed model achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models. |
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)
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| Challenge: | Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization. |
| Approach: | They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding. |
| Outcome: | The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou
| Challenge: | Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks. |
| Approach: | They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework. |
| Outcome: | The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks. |
A Simple yet Effective Learnable Positional Encoding Method for Improving Document Transformer Model (2022.findings-aacl)
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| Challenge: | Existing document Transformers lack a robust positional encoding mechanism to indicate and embed sequential order information in documents. |
| Approach: | They propose a positional encoding method that can be pre-trained on document datasets to improve document understanding. |
| Outcome: | The proposed method outperforms baselines on document understanding tasks in form, receipt, and invoice domains and is robust and stable on noisy data with incorrect order information. |
From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding (2023.acl-long)
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| Challenge: | Current models for natural language understanding require a preprocessing step to convert raw text into discrete tokens. |
| Approach: | They propose a hierarchical open-vocabulary language model that adopts a shallow Transformer architecture to learn word representations from their characters and a deep inter-word Transformer module that contextualizes each word representation by attending to the entire word sequence. |
| Outcome: | The proposed model outperforms baselines on various downstream tasks and is robust to textual corruption and domain shift. |