Papers by Weijia Zhao
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. |
GAN Driven Semi-distant Supervision for Relation Extraction (N19-1)
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| Challenge: | Existing methods for relation extraction are limited to costly hand-labeled training sets and hard to be extended to large-scale relations. |
| Approach: | They propose a semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. |
| Outcome: | The proposed approach achieves significant improvements over baselines on real-world datasets. |
Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction (2020.findings-emnlp)
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from low quality of test set, which leads to considerable biased performance evaluation. |
| Approach: | They propose a method to evaluate distantly supervised relation extraction using noisy test sets and manual annotations. |
| Outcome: | Experiments on a widely used benchmark show that the proposed method can yield approximately unbiased evaluations for distantly supervised relation extractors. |
Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations. |
| Approach: | They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules . |
| Outcome: | Extensive experiments show that the proposed model improves relation extraction. |
Design Challenges in Low-resource Cross-lingual Entity Linking (2020.emnlp-main)
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| Challenge: | Existing techniques for grounding mentions of entities in a foreign language do not rise to the challenges introduced by text in low-resource languages (LRL) and fail to generalize to text not taken from Wikipedia, on which they are usually trained. |
| Approach: | They propose a cross-lingual XEL technique that uses search engines to locate and search for foreign language entries in Wikipedia. |
| Outcome: | The proposed system shows an increase of 25% in gold candidate recall and 13% in end-to-end linking accuracy over state-of-the-art baselines. |
Getting MoRE out of Mixture of Language Model Reasoning Experts (2023.findings-emnlp)
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| Challenge: | Existing large language models (LLMs) have poor generalizability on question types beyond those seen in the prompt. |
| Approach: | They propose a framework that integrates specialized language models to generalize across question types that require distinct reasoning abilities. |
| Outcome: | The proposed framework gives higher accuracy than any single specialized model on a collection of 12 QA datasets from four reasoning types. |
Examining Gender Bias in Languages with Grammatical Gender (D19-1)
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| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
| Outcome: | The proposed methods reduce gender bias while preserving the original embeddings. |
CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit (2026.acl-long)
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| Challenge: | Diffusion large language models generate text through iterative denoising with bidirectional attention, enabling richer contextual dependencies. |
| Approach: | They propose a training-free parallel decoding method that fuses Trace Credit with current logits to boost the confidence of correct but underconfident tokens. |
| Outcome: | The proposed method achieves 5.48 times speedup with +0.48 accuracy on LLaDA-8B and is orthogonal to mainstream inference optimizations. |