Papers by Man Zhang
Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method (2025.acl-long)
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| Challenge: | Existing research on argument mining has proposed various argument annotation schemes and tasks. |
| Approach: | They propose a framework comprising 14 fine-grained relation types to capture the interplay between argument components for a thorough understanding of argument structure. |
| Outcome: | The proposed framework captures the interplay between argument components for a thorough understanding of argument structure. |
Generative Gamer: Learning Equilibrium Strategy by LLM-driven Dynamic Deduction (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) falter in domains requiring deep strategic reasoning. |
| Approach: | They propose a framework that trains LLMs to reason like an expert player . they propose action pruning based on policy confidence, state pruning via value estimation and branch pruning inspired by alpha-beta principles to train the model effectively. |
| Outcome: | Experiments on Tic-Tac-Toe and Leduc Poker show that GenGamer significantly improves the strategic capabilities of large language models. |
Can Multi-agent Help Disambiguation in Multi-domain Translation? (2026.findings-acl)
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| Challenge: | Existing multi-agent systems have shown strong potential for machine translation (MT) but their performance in multidomain translation remains unsatisfactory due to cross-domain word ambiguity . |
| Approach: | They propose a multi-agent collaborative disambiguation framework for MDT that leverages the collaborative capabilities of LLMs for disambiguations. |
| Outcome: | The proposed framework improves translation performance across multiple domains and improves disambiguation accuracy. |
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method (2024.findings-emnlp)
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Xinshu Shen, Hongyi Wu, Yadong Zhang, Man Lan, Xiaopeng Bai, Shaoguang Mao, Yuanbin Wu, Xinlin Zhuang, Li Cai
| Challenge: | Existing GEC datasets in Chinese fail to consider specific grammatical error types and overlook cross-sentence grammamatical errors. |
| Approach: | They propose to use Chinese essay fluency assessment to assess essay fluencies along with coarse and fine-grained errors and corrections to improve explainability. |
| Outcome: | The proposed dataset encapsulates essay fluency scores along with both coarse and fine-grained errors and corrections. |
MMDEND: Dendrite-Inspired Multi-Branch Multi-Compartment Parallel Spiking Neuron for Sequence Modeling (2025.acl-long)
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Kexin Wang, Yuhong Chou, Di Shang, Shijie Mei, Jiahong Zhang, Yanbin Huang, Man Yao, Bo Xu, Guoqi Li
| Challenge: | Vanilla spiking neurons are simplified from complex biological neurons with dendrites, soma, and synapses into single somatic compartments. |
| Approach: | They propose a multi-branch, multi-compartment parallel spiking dendritic neuron with a proportion-adjustable multi-branched structure that enables long-term temporal dependencies. |
| Outcome: | The proposed model achieves better long-sequence modeling capability with fewer parameters and lower energy consumption. |
Visually-Guided Policy Optimization for Multimodal Reasoning (2026.acl-long)
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| Challenge: | Existing RLVRs lack visual faithfulness due to text-dominated reasoning . a novel framework to reinforce visual focus during policy optimization is proposed . |
| Approach: | They propose a framework to reinforce visual focus during policy optimization using visual attention compensation mechanism. |
| Outcome: | The proposed framework exhibits better visual activation and superior performance in multimodal reasoning and visual-dependent tasks. |
DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation (2025.emnlp-main)
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| Challenge: | Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation, but their performance in multidomain translation (MDT) is less satisfactory. |
| Approach: | They propose to evaluate the disambiguation ability of Large Language Models in multi-domain translation . they construct a translation test set with multi- domain ambiguous word annotation . |
| Outcome: | The proposed framework evaluates LLMs on disambiguation in multi-domain translation (DMDTEval) the results show that LLM's perform poorly in multidomain translation, highlighting ambiguity in translation. |
Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation (2023.acl-long)
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| Challenge: | Existing methods for implicit discourse relation recognition (IDRR) lack connectives, which is a major challenge in discourse analysis research. |
| Approach: | They propose a method to predict latent correlations between connectives and discourse relations using a knowledge distillation approach. |
| Outcome: | The proposed method outperforms state-of-the-art models on coarse-grained and fine-grain discourse relations and can be transferred to explicit discourse relation recognition and achieve acceptable performance. |
ICL: Iterative Continual Learning for Multi-domain Neural Machine Translation (2024.findings-emnlp)
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| Challenge: | Existing studies have focused on learning domain knowledge from multiple domains, but task-specific parameters hinder mutual transfer of knowledge between new domains. |
| Approach: | They propose an iterative Continual learning framework for multi-domain neural machine translation that leverages previously acquired domain knowledge. |
| Outcome: | The proposed model outperforms baseline models on UM-Corpus and OPUS datasets. |
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)
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Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng
| Challenge: | Large Language Models (LLMs) have paved the way for complex tasks such as role-playing. |
| Approach: | They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models. |
| Outcome: | The proposed framework improves role-playing abilities with 168,093 samples. |
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning (2025.naacl-long)
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| Challenge: | Strategic reasoning requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. |
| Approach: | They propose a framework that enables Large Language Models to achieve varying levels of strategic depth by recursive mechanisms that allow agents to form higher order beliefs about others' beliefs. |
| Outcome: | The proposed framework enables LLMs to achieve varying levels of strategic depth, allowing agents to form higher order beliefs—beliefs about others’ beliefs. |
TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)
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Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, Man Lan
| Challenge: | Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback. |
| Approach: | They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations. |
| Outcome: | The proposed method significantly improves both automatic and human evaluations across four diverse LLMs. |
SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network (2024.acl-long)
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| Challenge: | SpikeVoice performs high-quality Text-To-Speech (TTS) via SNN . major obstacle to using SNN for such generative tasks lies in the demand for models to grasp long-term dependencies. |
| Approach: | They propose a brain-inspired Spiking Neural Network (SNN) which performs high-quality Text-To-Speech (TTS) via SNN and explores the potential of SNN to "speak". |
| Outcome: | The proposed model achieves comparable results to Artificial Neural Networks (ANN) with only 10.5% energy consumption of ANN. |