Papers by Jinyu Zhang
What Is Overlap Knowledge in Event Argument Extraction? APE: A Cross-datasets Transfer Learning Model for EAE (2023.acl-long)
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| Challenge: | Existing approaches ignore the overlap knowledge across datasets, preventing models from achieving better performance. |
| Approach: | They propose to divide the EAE knowledge into overlap knowledge across datasets and specific knowledge of the target dataset. |
| Outcome: | The proposed model outperforms the baseline model with a large margin when only ten records are available in the target dataset. |
Self-Supervised Prompt Optimization (2025.findings-emnlp)
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Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Xinbing Liang, Fengwei Teng, Jinhao Tu, Fashen Ren, Xiangru Tang, Sirui Hong, Chenglin Wu, Yuyu Luo
| Challenge: | Existing prompt optimization methods rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain. |
| Approach: | They propose a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without external reference. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt optimization methods with significantly lower costs and fewer samples. |
KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)
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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen
| Challenge: | Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings. |
| Approach: | They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method. |
| Outcome: | The proposed framework can be used in few-shot settings and reduces data requirements. |
Discarding the Crutches: Adaptive Parameter-Efficient Expert Meta-Learning for Continual Semantic Parsing (2025.coling-main)
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| Challenge: | Continual Semantic Parsing (CSP) enables parsers to generate SQL from natural language questions in task streams, using minimal annotated data to handle dynamically evolving databases in real-world scenarios. |
| Approach: | They propose a Adaptive PET eXpert meta-learning approach that assists experts in adaptively warming up, ensuring better model initialization. |
| Outcome: | The proposed method outperforms existing methods on two benchmarks and achieves superior performance without data replay or ideal settings. |
SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (2022.emnlp-main)
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| Challenge: | Existing models for pre-training text and speech are based on unlabeled audio data. |
| Approach: | They propose a unified-modal speech-unit-text pre-training model that connects speech encoders and text decoders with a shared unit encoder. |
| Outcome: | The proposed model improves on automatic speech recognition and speech translation tasks and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks. |
Closing the Modality Reasoning Gap for Speech Large Language Models (2026.acl-long)
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| Challenge: | Recent advances in Speech Large Language Models have a modality reasoning gap that is not addressed by prior work. |
| Approach: | They propose a reinforcement-learning framework that aligns text-conditioned and speech-conditioned trajectories through an asymmetric reward design. |
| Outcome: | Experiments on MMSU and OBQA show that the proposed framework narrows the modality reasoning gap and achieves state-of-the-art performance among 7B-scale Speech LLMs. |
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)
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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei
| Challenge: | Existing work shows that pre-trained models can improve in various natural language processing tasks. |
| Approach: | They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data. |
| Outcome: | The proposed framework is superior to existing models on speech-to-text processing tasks. |
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)
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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin
| Challenge: | Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks. |
| Approach: | They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset. |
| Outcome: | The proposed model performs well across tasks and languages. |