Papers by Zhijun Liu
Zero-shot Jianzi Recognition as Structured Visual Information Extraction in Open Compositional Symbolic Systems (2026.acl-long)
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| Challenge: | Optical character recognition (OCR) is a relatively new form of tablature recognition, but its accuracy is limited due to its unbounded composition and manuscript-level variability. |
| Approach: | They propose a method that predicts component sequences under a zero-shot split and synthesize manuscript-like training images via component-wise style recomposition and manuscript-domain noise modeling. |
| Outcome: | The proposed method achieves 63.02% sequence accuracy on real-world Jianzi benchmark, surpassing Gemini-3-Pro by 35.11%. |
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling (2022.emnlp-main)
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| Challenge: | Existing research treats Chinese character as a minimum unit for representation . however, such representation suffers from two bottlenecks: 1) learning bottleneck; 2) parameter bottleneck, each individual character has to be represented by a unique vector. |
| Approach: | They propose a representation method for Chinese characters to break the representation bottlenecks . they map each stroke to a specific Latin character, thus allowing similar Chinese characters . |
| Outcome: | The proposed representation method breaks two representation bottlenecks in Chinese character representation . it maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar representations . |
Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)
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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang
| Challenge: | Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. |
| Approach: | They propose a model merging framework that modulates the contribution of each source model. |
| Outcome: | Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages. |