Papers by Yuanyuan Wang

OD-RTE: A One-Stage Object Detection Framework for Relational Triple Extraction (2023.acl-long)

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Jinzhong Ning, Zhihao Yang, Yuanyuan Sun, Zhizheng Wang, Hongfei Lin

Challenge:	Existing pipelines for relational triple extraction are underutilizing regional information of triple.
Approach:	They propose a one-stage Object Detection framework for Relational Triple Extraction . framework uses vertices-based bounding box detection and global relational triple region detection .
Outcome:	The proposed framework could extract all types of triples on two widely used datasets.

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

Green & Sustainable NLP

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.

SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)

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Han Liu, Shuqin Li, Xiaotong Zhang, Yuanyuan Wang, Feng Zhang, Hongyang Chen, Hong Yu

Information Retrieval

Challenge:	Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance.
Approach:	They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories.
Outcome:	The proposed method significantly outperforms state-of-the-art models.

Retrieval Augmented Instruction Tuning for Open NER with Large Language Models (2025.coling-main)

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Tingyu Xie, Jian Zhang, Yan Zhang, Yuanyuan Liang, Qi Li, Hongwei Wang

Semantic Text Processing

Challenge:	Existing studies have focused on integrating large language models (LLMs) with information extraction (IE) however, the best approach to incorporate information with LLMs for IE remains an open question.
Approach:	They propose to use a Chinese IT dataset to perform RA-IT for IE . they use semantically similar examples from the training dataset as the context .
Outcome:	The proposed approach is evaluated in English and Chinese scenarios.

Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)

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Haoyu Zhang, Yu Wang, Guanghao Yin, Kejun Liu, Yuanyuan Liu, Tianshu Yu

Multimodality

Challenge:	Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved.
Approach:	They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales.
Outcome:	The proposed model achieves state-of-the-art on several popular datasets and an abundance of ablation shows the effectiveness of the proposed model.

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)

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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems.
Approach:	They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision.
Outcome:	The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks.

Prototype Tuning: A Meta-Learning Approach for Few-Shot Document-Level Relation Extraction with Large Language Models (2025.findings-naacl)

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Dinghao Pan, Yuanyuan Sun, Bo Xu, Jiru Li, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

Language Models

Challenge:	Few-Shot Document-Level Relation Extraction (FSDLRE) aims to develop models capable of generalizing to new categories with minimal support examples.
Approach:	They propose a meta-training approach to train Large Language Models to improve their ICL capabilities . they construct simulated episodes using relation types that do not overlap with test corpus .
Outcome:	Experimental results show that the proposed approach outperforms baseline models on few-shot tasks.

COCOGEC: Counterfactual Generation for Robust Grammatical Error Correction (2026.findings-acl)

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Qianyu Wang, Xiaoman Wang, Yuanyuan Liang, Xinyuan Li, Yunshi Lan

Robustness in NLP

Challenge:	Existing GEC models fail to understand error patterns in varying contexts . a framework that generates copies of training instances with error-irrelevant contexts altered is proposed .
Approach:	They propose a framework that generates copies of training instances with error-irrelevant contexts altered.
Outcome:	The proposed framework outperforms baselines on the simulated tasks and outperformed existing models.

Knowledge Vector of Logical Reasoning in Large Language Models (2026.acl-long)

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Zixuan Wang, Yuanyuan Lei

Semantic Text Processing

Challenge:	Logical reasoning is a central capability in LLMs, but understanding their abilities remains poorly understood.
Approach:	They propose to refine the knowledge representations of each reasoning type in LLMs to encourage complementarity . they propose to use complementary loss and subspace constraint loss to enhance complementarities .
Outcome:	The proposed framework encourages complementarity between the different types of reasoning in LLMs.

Demystifying Data Organization for Enhanced LLM Training (2026.acl-long)

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Yalun Dai, Yangyu Huang, Tongshen Yang, Yonghan Wang, Xin Zhang, Wenshan Wu, Qihao Zhao, Hao Li, Yuanyuan Gao, Kim-Hui Yap, Scarlett Li

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation.
Approach:	They propose to reuse pre-computed sample-level scores originally generated for data efficiency and introduce two new data ordering methods to improve LLM training.
Outcome:	The proposed methods improve the stability and performance of LLM training.

Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation (2023.emnlp-main)

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Yuanyuan Liang, Jianing Wang, Hanlun Zhu, Lei Wang, Weining Qian, Yunshi Lan

Semantic Text Processing

Challenge:	Existing methods for question generation over knowledge bases rely on annotated data for fine-tuning . emergence of Large Language Models (LLMs) has shown impressive generalization ability in few-shot tasks.
Approach:	They propose to use a logical form to generate a question in a reasoning problem . they propose to extend the prompting method into a method that can generate questions in logical forms .
Outcome:	The proposed method outperforms baselines on three public KBQG datasets.

Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)

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Jinzhong Ning, Zhihao Yang, Zhizheng Wang, Yuanyuan Sun, Hongfei Lin, Jian Wang

Information Extraction & Text Mining

Challenge:	Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities.
Approach:	They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese.
Outcome:	The proposed model can learn the interaction of bilinguals and dependent information within Chinese.

Sheep’s Skin, Wolf’s Deeds: Are LLMs Ready for Metaphorical Implicit Hate Speech? (2025.acl-long)

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Jingjie Zeng, Liang Yang, Zekun Wang, Yuanyuan Sun, Hongfei Lin

Responsible & Trustworthy NLP

Challenge:	specialized models fail to detect implicit hate speech due to its indirectly expressed hateful intent . advanced LLMs often misinterpret metaphorical implicit hate content, resulting in its propagation .
Approach:	They propose a Jailbreaking strategy and Energy-based Constrained Decoding techniques to detect implicit hate speech in large language models.
Outcome:	The proposed model can generate metaphorical implicit hate speech, but it fails to detect it effectively.

Sentence-level Media Bias Analysis Informed by Discourse Structures (2022.emnlp-main)

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Yuanyuan Lei, Ruihong Huang, Lu Wang, Nick Beauchamp

Responsible & Trustworthy NLP

Challenge:	Recent work on detecting media bias at the level of individual articles is limited to single sentences.
Approach:	They propose to use a news discourse structure and PDTB discourse relations to identify bias sentences within an article that can illuminate and explain the overall bias of the entire article.
Outcome:	The proposed model can detect bias at the level of individual articles and a single sentence can explain it.

UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment (2026.acl-long)

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Yuanyuan Wang, Dongchao Yang, Yayue Deng, Zhiyong Wu, Steven Y. Guo, Helen M. Meng, Xixin Wu

Multimodality

Challenge:	Existing methods for speech generation rely on subjective, expensive judgments . Existing models only cover a narrow set of scenarios and only provide limited coverage .
Approach:	They propose a unified speech reward model that can support multi-dimensional, interpretable reward signals with reliable reasoning.
Outcome:	The proposed model can support multi-dimensional, interpretable reward signals with reliable reasoning.

Polarity Calibration for Opinion Summarization (2024.naacl-long)

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Yuanyuan Lei, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Ruihong Huang, Dong Yu

Sentiment Analysis

Challenge:	Existing opinions summarization models emphasize the majority opinions while ignoring the minority opinions.
Approach:	They propose a method to align output summary and input text to achieve polarity calibration.
Outcome:	The proposed model can mitigate the polarity mismatch between output summary and input text, and maintain the content semantic and language quality.