Papers by Tao Qian
From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)
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| Challenge: | Existing models rely on pre-trained language models, which have a maximum input sequence length of 512 tokens, and therefore have 'input length limitation'. |
| Approach: | They propose a text segmentation algorithm which guarantees to produce the optimal segmentation to address the issue of input length limitation caused by PLMs. |
| Outcome: | The proposed method improves both text and label representations on MLTC datasets, unraveling the intricate correlations between texts and labels. |
UniLG: A Unified Structure-aware Framework for Lyrics Generation (2023.acl-long)
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| Challenge: | Existing works ignore musical attributes hidden behind lyrics and structure of lyrics . existing works ignore structure of generated lyrics and do not consider structure of songs . |
| Approach: | They propose a framework for conditional lyrics generation that considers structure and relationship between lyrics and music. |
| Outcome: | The proposed framework improves the structure modeling and unifies different conditions for different types of lyrics generation. |
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. |
AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations (2025.coling-main)
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| Challenge: | Large language models require substantial storage space to perform tasks such as text generation and video generation. |
| Approach: | They propose to compress large language models using integer replacements for floating-point numbers, in a process known as Quantization. |
| Outcome: | The proposed model allows for quantization of up to 75% decoder layers with 1 bit while maintaining performance levels comparable to those of the models with floating parameters. |
TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation rely on fragment-level retrieval . GraphRAG suffers from inefficiencies in information extraction and costly resource consumption . |
| Approach: | They propose a tag-guided hierarchical knowledge graph RAG framework for efficient global reasoning and scalable graph maintenance. |
| Outcome: | GraphRAG achieves an average win rate of 78.36% on a dataset spanning agriculture, computer science, law, and cross-domain settings compared with baselines . |
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. |
| Approach: | They propose a data-aware metric called ISS to measure the final performance of MRs. |
| Outcome: | The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process. |
EvoR: Evolving Retrieval for Code Generation (2024.findings-emnlp)
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| Challenge: | Existing pipelines for retrieval-augmented code generation (RACG) use static knowledge bases with a single source, limiting adaptation capabilities of Large Language Models (LLMs) Extensive experiments demonstrate that EVOR achieves two to four times of execution accuracy compared to other methods such as Reflexion. |
| Approach: | They propose a retrieval-augmented code generation pipeline that employs the synchronous evolution of queries and diverse knowledge bases. |
| Outcome: | The proposed pipeline achieves two to four times of execution accuracy compared to other methods. |
Measuring Human Contribution in AI-Assisted Content Generation (2026.acl-long)
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Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu
| Challenge: | generative AI has created a new way to generate content with humans . varying degrees of human contribution in content generation poses significant challenges for the delineation of originality . |
| Approach: | They propose a framework to measure human contribution in AI-assisted content generation by calculating mutual information between human input and AI-aided output relative to self-information of AI-assist output. |
| Outcome: | The proposed measure discriminates between varying degrees of human contribution across multiple creative domains and is validated in real-world applications. |
A Structure-Aware Generative Adversarial Network for Bilingual Lexicon Induction (2023.findings-emnlp)
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| Challenge: | Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. |
| Approach: | They propose a model that explicitly captures multiple topological structure information to achieve accurate bilingual lexicon induction. |
| Outcome: | The proposed model captures multiple topological structure information to achieve accurate BLI on a public dataset. |
Improving Text Generation with Student-Forcing Optimal Transport (2020.emnlp-main)
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Jianqiao Li, Chunyuan Li, Guoyin Wang, Hao Fu, Yuhchen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
| Challenge: | Maximum likelihood estimation (MLE) is used to train models, but during testing, the model is conditioned on previously generated tokens, resulting in exposure bias. |
| Approach: | They propose to use optimal transport to match the sequences generated in MLE and test modes to reduce exposure bias. |
| Outcome: | The proposed method is validated on machine translation, text summarization, and text generation tasks. |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
| Outcome: | The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines. |