Papers by Boyu Cao
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)
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| Challenge: | Recent studies have shown that using external knowledge such as pre-trained word embeddings or pre-train language models only achieved limited performance improvements but with huge computational overhead. |
| Approach: | They propose to incorporate external knowledge into neural topic modeling by pre-trained word embeddings (PWEs) or pre-train language models (PLMs) they propose to fine-tune the neural topic model on the target dataset and reduce the huge size of training data. |
| Outcome: | The proposed approach outperforms current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs on three datasets and greatly reduces the huge size of training data. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Anchored Cyclic Generation: A Novel Paradigm for Long-Sequence Symbolic Music Generation (2026.findings-acl)
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| Challenge: | Autoregressive sequence modeling has been successful in many domains, but maintaining long-term coherence and structural integrity remains a challenge. |
| Approach: | They propose an ACG paradigm that relies on anchor features from previously generated musical content to guide subsequent generation during the autoregressive process. |
| Outcome: | The proposed framework outperforms existing methods in symbolic music generation tasks. |
Neural Topic Modeling based on Cycle Adversarial Training and Contrastive Learning (2023.findings-acl)
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| Challenge: | Neural topic models have been widely used to extract common topics across documents. |
| Approach: | They propose a framework to apply contrastive learning directly to the decoder . they propose 'self-supervised' contrastive loss to make the generator capture similar topic information . |
| Outcome: | The proposed framework outperforms baselines on four benchmark datasets. |
iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations (2026.acl-long)
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| Challenge: | Lack of causally annotated text data for use as ground truth hinders causal discovery . early template-based generation methods sacrifice text naturalness in exchange for high annotation costs . |
| Approach: | They propose a method which performs real-world concept assignment to nodes before converting causal graphs into text. |
| Outcome: | The proposed method shows high annotation accuracy and naturalness across extensive tests. |