Papers by Zheng Liang

SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)

Challenge:	PaddleSpeech is an open-source speech toolkit that supports speech-to-text and text-to speech tasks.
Approach:	They describe the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to speech tasks.
Outcome:	The proposed framework achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods.

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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao

CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning (2022.naacl-industry)

Challenge:	Existing frameworks that increase context window do not guarantee robust performance across long input tasks.
Approach:	They propose a framework that enables language models to handle extended inputs within limited context windows efficiently.
Outcome:	The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks.

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Penghui Wei, Xuanhua Yang, ShaoGuo Liu, Liang Wang, Bo Zheng

CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models (2024.findings-acl)

Challenge:	a paper focuses on automatically generating the text of an ad to capture user interest for achieving higher click-through rate.
Approach:	They propose a CTR-driven advertising text generation approach to generate ad texts based on user reviews.
Outcome:	The proposed approach outperforms existing approaches on industrial datasets and on large-scale unpaired reviews.

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Qian Lou, Xin Liang, Jiaqi Xue, Yancheng Zhang, Rui Xie, Mengxin Zheng

Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding (2022.findings-acl)

Challenge:	Existing certified robustness methods for certifying input-specific text perturbations have shown promise in certifyling UTPs, but masking only adversarial words can eliminate the attack.
Approach:	They propose a method to certify a language model’s robustness against UTPs by using random smoothing.
Outcome:	The proposed method achieves high certified accuracy under extensive masking and achieves state-of-the-art results in multiple settings.

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Rui Cao, Yihao Wang, Yuxin Liang, Ling Gao, Jie Zheng, Jie Ren, Zheng Wang

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

Challenge:	Unsupervised contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data.
Approach:	They propose a momentum contrastive learning model with negative sample queue for sentence embedding with a simulated model with EMA update mechanism.
Outcome:	The proposed model achieves a Spearman’s correlation of 77.27% on the semantic text similarity task and a maximum traceable distance metric.

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Baigong Zheng, Renjie Zheng, Mingbo Ma, Liang Huang

Intrinsic Mutual Information as a Modulator for Preference Optimization (2026.findings-acl)

Challenge:	Recent work on simultaneous translation is difficult because of its latency and quality.
Approach:	They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information .
Outcome:	Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work.

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Peng Liao, Peijia Zheng, Lingbo Li, Shangsong Liang, Lin Chen

Challenge:	Existing methods for offline preference optimization involve additional hyperparameter tuning, resulting in substantial time overhead.
Approach:	They propose a lightweight framework for offline preference optimization that leverages hyperparameter modulation to decouple preference contributions.
Outcome:	The proposed framework achieves superior performance over existing methods while reducing training overhead by more than 15%.

RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)

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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang

Hidden States as Early Signals: Step-level Trace Evaluation and Pruning for Efficient Test-Time Scaling (2026.findings-acl)

Challenge:	Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed.
Approach:	They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting.
Outcome:	The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data.

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Zhixiang Liang, Beichen Huang, Zheng Wang, Minjia Zhang

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Existing approaches to speed up parallel scaling have relied on similarity-based or confidence-based pruning, but these signals do not reliably indicate trace quality.
Approach:	They propose a pruning framework that evaluates reasoning steps using hidden states and dynamically prunes unpromising traces during generation.
Outcome:	The proposed framework reduces end-to-end inference latency by 45%–70% on average compared to self-consistency while improving reasoning accuracy.

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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.

AdaV: Adaptive Text-visual Redirection for Vision-Language Models (2025.findings-acl)

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Jiayi Han, Liang Du, Yiwen Wu, Guanming Liang, Xiangguo Zhou, Weibo Zheng, Donghong Han, Zixun Sun

Visual Data in NLP

Challenge:	Vision-language models often generate excessive visual tokens, leading to poor performance . a novel training-free visual token pruning method is proposed to improve performance despite the computational cost associated with VLMs.
Approach:	They propose a training-free visual token pruning method that reduces biased token pruning . they plan to open-source the code upon publication .
Outcome:	The proposed method reduces biased token pruning and enhances model robustness with limited visual token budget.

EXPLAIN: Enhancing Retrieval-Augmented Generation with Entity Summary (2025.acl-industry)

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Yaozhen Liang, Xiao Liu, Jiajun Yu, Zhouhua Fang, Qunsheng Zou, Linghan Zheng, Yong Li, Zhiwei Liu, Haishuai Wang

Explainability & Interpretability in NLP

Challenge:	Existing document question answering methods reduce inference costs and input tokens.
Approach:	They propose a retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents.
Outcome:	The proposed method surpasses baseline retrieval-augmented generation (RAG) and long-context question answering (LC) methods achieve higher accuracy by processing entire documents, but at the cost of increased computational Corresponding authors.

Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)

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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin

Towards Debiasing Sentence Representations (2020.acl-main)

Challenge:	Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization.
Approach:	They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach.
Outcome:	The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks.

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Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency

Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)

Challenge:	Recent work has shown word-level embeddings reflect and propagate social biases present in training corpora.
Approach:	They propose a method to debias word embeddings to reduce biases at sentence level . they hope their work will inspire future research on characterizing and removing biase .
Outcome:	The proposed method reduces biases and preserves performance on downstream tasks such as sentiment analysis and natural language understanding.

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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang

Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR (2021.findings-acl)

Challenge:	Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials.
Approach:	They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch.
Outcome:	The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks.

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Junkun Chen, Mingbo Ma, Renjie Zheng, Liang Huang

Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (2025.findings-acl)

Challenge:	Existing approaches to simultaneous speech-to-text translation suffer from error propagation and extra latency.
Approach:	They propose a new paradigm for simultaneous speech-to-text translation using two separate decoders . they use multitask learning to jointly learn these two tasks with a shared encoder .
Outcome:	The proposed method achieves substantially better translation quality at similar levels of latency.

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Yongsen Zheng, Zongxuan Xie, Guohua Wang, Ziyao Liu, Liang Lin, Kwok-Yan Lam

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models (2025.emnlp-main)

Challenge:	Unfairness is a well-known challenge in Recommender Systems (RSs) some approaches have started to improve fairness in offline or static contexts, but it often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers.
Approach:	They propose a framework to promote multi-interest diversity fairness in RSs by establishing diverse hypergraphs through contrastive learning.
Outcome:	The proposed framework achieves state-of-the-art performance while effectively alleviating unfairness in two CRS-based datasets.

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Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)

Challenge:	Current approaches focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context.
Approach:	They propose a benchmark to evaluate how large vision language models understand memes in their original context.
Outcome:	The proposed benchmark evaluates how large vision language models understand meme intent in their original context.

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Qingyue Wang, Liang Ding, Yanan Cao, Yibing Zhan, Zheng Lin, Shi Wang, Dacheng Tao, Li Guo

Simultaneous Translation with Flexible Policy via Restricted Imitation Learning (P19-1)

Challenge:	Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system.
Approach:	They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism.
Outcome:	The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism.

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Baigong Zheng, Renjie Zheng, Mingbo Ma, Liang Huang

Machine Translation

Challenge:	Existing approaches to simultaneous translation have been limited and use fixed-latency policies or a complicated two-staged model.
Approach:	They propose a single model that adds a “delay” token to the target vocabulary and a restricted dynamic oracle to greatly simplify training.
Outcome:	The proposed model achieves better BLEU scores and lower latencies compared to fixed and RL-learned policies on Chinese -> English simultaneous translation.

POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation (2024.acl-long)

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Shilong Pan, Zhiliang Tian, Liang Ding, Haoqi Zheng, Zhen Huang, Zhihua Wen, Dongsheng Li

STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)

Challenge:	Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data.
Approach:	They propose a method that uses a dynamic graph to organize auxiliary languages in prompts to improve LRL translations.
Outcome:	The proposed method improves translation accuracy in low-resource languages (LRLs) using auxiliary language pairs and synthetic pseudo-parallel data.

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Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang

Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)

Challenge:	Simultaneous translation is notoriously dif- ficult due to word-order differences.
Approach:	They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model.
Outcome:	The proposed framework achieves low latency and reasonable qual- ity on 4 directions.

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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji

Uncertainty Aware Learning for Language Model Alignment (2024.acl-long)

Challenge:	Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge.
Approach:	They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities.
Outcome:	The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict.

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Yikun Wang, Rui Zheng, Liang Ding, Qi Zhang, Dahua Lin, Dacheng Tao

Simultaneous Translation Policies: From Fixed to Adaptive (2020.acl-main)

Challenge:	Existing alignment strategies that focus on diverse and high-quality data often overlook the intrinsic uncertainty of tasks, learning all data samples equally.
Approach:	They propose to introduce the sample uncertainty into the alignment of different task scenarios by a simple fashion by setting the label smoothing value of training according to the uncertainty of individual samples.
Outcome:	The proposed model outperforms standard supervised fine-tuning on high-entropy tasks and complex low-entropic tasks.

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Baigong Zheng, Kaibo Liu, Renjie Zheng, Mingbo Ma, Hairong Liu, Liang Huang

Opportunistic Decoding with Timely Correction for Simultaneous Translation (2020.acl-main)

Challenge:	Adaptive policies can balance translation quality and latency based on context information . previous methods on obtaining adaptive policies rely on complicated training process .
Approach:	They propose to obtain adaptive policies by a simple heuristic composition of fixed policies . they propose to use a heurism to obtain policies that can outperform fixed ones .
Outcome:	Experiments on Chinese -> English and German -> english show that adaptive policies outperform fixed policies by up to 4 BLEU points for the same latency.

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Renjie Zheng, Mingbo Ma, Baigong Zheng, Kaibo Liu, Liang Huang

Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training (2020.findings-emnlp)

Challenge:	Existing approaches to balancing translation quality and latency are either too aggressive or too conservative.
Approach:	They propose an opportunistic decoding technique that always (over-)generates a certain mount of extra words at each step to keep the audience on track with the latest information.
Outcome:	The proposed technique reduces latency and increases BLEU with no over-generating . it also corrects mistakes in the overgenerated words when observing more context .

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Renjie Zheng, Mingbo Ma, Baigong Zheng, Kaibo Liu, Jiahong Yuan, Kenneth Church, Liang Huang

Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation (D18-1)

Challenge:	Current approaches to simultaneous speech-to-speech translation accumulate more and more latencies in later sentences when the speaker talks faster.
Approach:	They propose a method which generates more fluent target speech latency than the baseline . they propose to use self-adaptive translation to adjust the length of translations to accommodate different source speech rates.
Outcome:	Xiong et al., 2019) show that the proposed method generates more fluent target speech latency than baseline . authors say it provides more natural communication process than speech-to-text translation . xiong and colleagues say the proposed technique is more efficient than current approaches .

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Renjie Zheng, Mingbo Ma, Liang Huang

Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs (2025.coling-main)

Challenge:	Neural text generation has been quite successful recently, but during training time, only one reference is considered for each example, even though there are often multiple references available.
Approach:	They propose an algorithm to generate exponentially many pseudo-references by compressing existing references into lattices and traversing them to generate new pseudo-References.
Outcome:	The proposed model significantly improves on baselines in machine translation and image captioning, and is comparable to existing models.

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Chuang Liu, Renren Jin, Zheng Yao, Tianyi Li, Liang Cheng, Mark Steedman, Deyi Xiong

Challenge:	Existing benchmarks for evaluating large language models neglect key qualitative data attributes that can significantly impact the final rankings of LLMs.
Approach:	They propose a framework with three modules designed to assess diversity, redundancy, and difficulty.
Outcome:	The proposed framework systematically incorporates diversity, redundancy, and difficulty attributes and shows that they influence the ranking of LLMs.

MKT: A Multi-Stage Knowledge Transfer Framework to Mitigate Catastrophic Forgetting in Multi-Domain Chinese Spelling Correction (2025.emnlp-industry)

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Peng Xing, Yinghui Li, Shirong Ma, Xinnian Liang, Haojing Huang, Yangning Li, Shu-Yu Guo, Hai-Tao Zheng, Wenhao Jiang, Ying Shen

Green & Sustainable NLP

Challenge:	Chinese Spelling Correction (CSC) is a model that detects and corrects spelling errors in given sentences.
Approach:	They propose a model-agnostic model with an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain rather than focusing solely on new domain knowledge.
Outcome:	The proposed model-agnostic framework is based on an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain, rather than focusing solely on new domain knowledge.

Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems (2026.acl-long)

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Xingchen Zou, Yuhao Yang, Zheng Chen, Xixuan Hao, Yiqi Chen, Chao Huang, Yuxuan Liang

TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)

Challenge:	Rapid urbanization and surging vehicle ownership intensify congestion . rapid urbanization drives crash rates, slow emergency response, and burden transit-poor communities .
Approach:	They introduce a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC) they use reinforcement learning and network communication to convert LLM into a traffic-control model that operates like a human traffic agent.
Outcome:	The proposed model outperforms baselines and training-intensive RL controllers on a simulated traffic environment and reduces queue lengths by more than 5%.

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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu

Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)

Challenge:	Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning.
Approach:	They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.
Outcome:	The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.

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Yue Wang, Hua Zheng, Yaqi Yin, Hansi Wang, Qiliang Liang, Yang Liu

Visual Data in NLP

Challenge:	Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods.
Approach:	They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks.
Outcome:	The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word .

VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs (2025.emnlp-main)

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Keer Lu, Keshi Zhao, Zhuoran Zhang, Zheng Liang, Bin Cui, Tengjiao Wang, Wentao Zhang

MixRED: A Mix-lingual Relation Extraction Dataset (2024.lrec-main)

Challenge:	Existing work focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains.
Approach:	They propose a data composition framework that allows LLMs to enhance their multi-domain capabilities during supervised fine-tuning.
Outcome:	The proposed framework improves multi-domain fostering performance by 29.77% compared to uniform weights.

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Lingxing Kong, Yougang Chu, Zheng Ma, Jianbing Zhang, Liang He, Jiajun Chen

Information Extraction & Text Mining

Challenge:	Existing research focuses on monolingual relation extraction, but there is a significant gap in understanding relation extraction in the mix-lingual scenario.
Approach:	They propose a task of considering relation extraction in the mix-lingual scenario . they construct a human-annotated dataset to support the task .
Outcome:	The proposed task evaluates state-of-the-art supervised models and large language models on the human-annotated dataset MixRED.

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)

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Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang, Ruihui Zhao, Bang Liu, Xiaodan Liang, Yefeng Zheng

Towards Quantifiable Dialogue Coherence Evaluation (2021.acl-long)

Challenge:	Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems.
Approach:	They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies.
Outcome:	The proposed system significantly outperforms baselines in both dialogue generation and strategy planning.

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Zheng Ye, Liucun Lu, Lishan Huang, Liang Lin, Xiaodan Liang

Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)

Challenge:	Existing automatic dialogue coherence evaluation metrics are expensive and high-latency, which cannot meet the requirements of a dialogue system.
Approach:	They propose a framework to train a quantifiable dialogue coherence metric that can reflect actual human rating standards.
Outcome:	Experimental results show that the model trained by QuantiDCE presents stronger correlations with human judgements than the other state-of-the-art metrics.

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Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou

Summarization

Challenge:	Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions.
Approach:	They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization.
Outcome:	The proposed model outperforms the state-of-the-art models in the zero-shot directions.

Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization (2024.lrec-main)

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Linzhi Wu, Xingyu Zhang, Yakun Zhang, Changyan Zheng, Tiejun Liu, Liang Xie, Ye Yan, Erwei Yin

PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering (2026.acl-long)

Challenge:	Lip reading is a process of interpreting silent speech from visual lip movements . but lip reading in cross-speaker scenarios poses a challenging problem due to inter-speech variability .
Approach:	They propose to exploit lip landmark-guided visual clues instead of mouth-cropped images as input features.
Outcome:	Experimental results show that the proposed approach reduces speaker-specific appearance characteristics in cross-speaker scenarios.

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Xiangfeng Wang, Hangyu Guo, Yanlin Lai, Mitt Huang, Liang Zhao, Chengyuan Yao, Yinmin Zhang, Qi Han, null Xiaoxiaoren, Chun Yuan, Tong Xu, Zheng Ge, Xiangyu Zhang, Daxin Jiang

Eureka: Neural Insight Learning for Knowledge Graph Reasoning (2022.coling-1)

Challenge:	Current outcome-centric verification paradigms neglect potential errors in the derivation process.
Approach:	They propose a process-aware RLVR training paradigm utilizing verifiers selected via PRIME.
Outcome:	The proposed approach outperforms the baseline verification paradigm on AIME24, AIME25, and Beyond-AIME models.

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Alex X. Zhang, Xun Liang, Bo Wu, Xiangping Zheng, Sensen Zhang, Yuhui Guo, Jun Wang, Xinyao Liu

Structured Data in NLP

Challenge:	Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples .
Approach:	They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger .
Outcome:	The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples .

HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)

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Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin

Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings (2021.emnlp-main)

Challenge:	Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time.
Approach:	They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.
Outcome:	The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks.

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Junkun Chen, Renjie Zheng, Atsuhito Kita, Mingbo Ma, Liang Huang

DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)

Challenge:	Existing systems for simultaneous translation are still trained on full-sentence bitexts due to the abundance of unnecessary long-distance reorderings.
Approach:	They propose to rewrite target side of existing full-sentence corpora into simultaneous-style translation by adding generated pseudo-references to the target side.
Outcome:	Experiments on ZhEn and JaEn simultaneous translation show that the proposed method improves on existing full-sentence corpora.

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Wei Fan, Wenlin Yao, Zheng Li, Feng Yao, Xin Liu, Liang Qiu, Qingyu Yin, Yangqiu Song, Bing Yin

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage.
Approach:	They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents.
Outcome:	Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget.

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Yijun Shen, Delong Chen, Fan Liu, Xingyu Wang, Chuanyi Zhang, Liang Yao, Yuhui Zheng

Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees (2026.acl-long)

Challenge:	Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment .
Approach:	They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints.
Outcome:	The proposed method improves annotation speed and retrieval performance over the parallel method.

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Zheng Zhang, Qi Liu, Siyuan Liang, Ning Li, Zirui Hu, Weibo Gao, Rui Li, Zhenya Huang, Leszek Rutkowski, Baosheng Yu, Dacheng Tao

SEEK: Segmented Embedding of Knowledge Graphs (2020.acl-main)

Challenge:	Existing training data detectors fail to detect clean samples from contaminated test sets . existing methods fail to identify clean samples due to black-box nature of LLMs .
Approach:	They propose a framework that detects and filters contaminated evaluation data . they propose 'failure detection' to reduce the proportion of contaminated samples mistakenly retained .
Outcome:	The proposed framework reduces false discovery rate (FDR) under valid FDR control while maintaining evaluation consistency.

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Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu

G-Cap: A Game Character Caption Generator (2026.acl-long)

Challenge:	Existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them far from satisfactory.
Approach:	They propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity.
Outcome:	The proposed framework can achieve highly competitive relational expressiveness without increasing model complexity.

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Yang Yang, Feng Hu, Haiming Zhang, XU Cheng, Gui Zheng, Liang Yao, Wenqi Ren

Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)

Challenge:	Existing studies on Large Vision-Language Models (LVLMs) primarily focus on real-world scenarios, leaving surreal, highly stylized, and semantically hybrid virtual-world situations significantly underexplored.
Approach:	They propose to use a manually annotated benchmark to evaluate LVLMs' ability to perceive and describe game character from the virtual-world.
Outcome:	The proposed task evaluates LVLMs’ ability to perceive and describe game character from the virtual-world.

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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao

HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025.findings-acl)

Challenge:	Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial.
Approach:	They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training.
Outcome:	The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training.

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Yongsen Zheng, Mingjie Qian, Guohua Wang, Yang Liu, Ziliang Chen, Mingzhi Mao, Liang Lin, Kwok-Yan Lam

Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)

Challenge:	Existing methods to analyze filter bubbles in the static recommendation environment are unable to burst them during user interactions.
Approach:	They propose a paradigm to learn multi-grained user preferences during dynamic user-system interactions via natural language conversations to burst filter bubbles.
Outcome:	The proposed paradigm achieves state-of-the-art performance and the superior of bursting filter bubbles in the conversational recommendation system.

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Rui Zheng, Bao Rong, Yuhao Zhou, Di Liang, Sirui Wang, Wei Wu, Tao Gui, Qi Zhang, Xuanjing Huang

Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution (2025.findings-acl)

Challenge:	Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples.
Approach:	They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search.
Outcome:	The proposed method improves on previous work on adversarial robustness evaluation.

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Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin

Learning from the Irrecoverable: Error-Localized Policy Optimization for Tool-Integrated LLM Reasoning (2026.acl-long)

Challenge:	OpenAI's O1 and subsequent projects like DeepSeek R1 have significantly advanced research on complex reasoning in LLMs.
Approach:	They analyze existing reasoning studies from the perspective of self-evolution and summarize O1-like works from open-source projects like DeepSeek R1 and Kimi-k1.5.
Outcome:	The proposed models are based on open-source models and pioneer advanced methodologies like Scaling Reinforcement Learning (RL).

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Qiao Liang, Yuke Zhu, Chao Ge, Lei Yang, Ying Shen, Bo Zheng, Sheng Guo

Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition (2022.emnlp-main)

Challenge:	Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning suffers from sparse, delayed rewards and weak step-level credit assignment.
Approach:	They propose a tool-integrated reasoning approach that localizes the first irrecoverable step and leverages it for fine-grained credit assignment.
Outcome:	The proposed algorithm outperforms strong Agentic RL benchmarks in math, science QA, and code execution with additional gains in Pass@K and Major@K scaling, rollout ranking quality, and tool-call efficiency.

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Junhao Zheng, Zhanxian Liang, Haibin Chen, Qianli Ma

T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering (2025.emnlp-main)

Challenge:	Existing methods for Named Entity Recognition (NER) are not able to learn Other-Class in the same way as new entity types.
Approach:	They propose a unified causal framework to retrieve causality from new entity types and Other-Class.
Outcome:	The proposed method outperforms the state-of-the-art method on three benchmark datasets.

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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Question Answering

Challenge:	Existing efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions but add human bias to the reasoning process.
Approach:	They propose a framework that dynamically adapts reasoning depth based on question complexity.
Outcome:	Experimental results show that the proposed framework achieves higher accuracy than baseline methods and reduces computational overhead by up to 25.2%.

Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation (2026.acl-long)

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Qianchi Zhang, Hainan Zhang, Liang Pang, Hong-Wei Zheng, Zhiming Zheng

Speculative Beam Search for Simultaneous Translation (D19-1)

Challenge:	Existing RAG methods focus on enhancing LLM robustness to low-quality retrieval, but neither address permutation sensitivity.
Approach:	They propose a method that exploits permutation sensitivity to mitigate hallucinations in Large Language Models.
Outcome:	The proposed model improves answer accuracy, reasoning consistency, and generalization across datasets, retrievers, and input lengths compared with strong baselines.

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Renjie Zheng, Mingbo Ma, Baigong Zheng, Liang Huang

CASN:Class-Aware Score Network for Textual Adversarial Detection (2023.acl-long)

Challenge:	Beam search is widely used in (full-sentence) machine translation but its application to simultaneous translation remains highly non-trivial.
Approach:	They propose a beam search algorithm that hallucinates several steps into the future to reach a more accurate decision by implicitly benefiting from a target language model.
Outcome:	The proposed method improves on language models over diverse language pairs and shows significant improvements over greedy search.

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Rong Bao, Rui Zheng, Liang Ding, Qi Zhang, Dacheng Tao

EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models (2026.acl-long)

Challenge:	Existing approaches to counteract adversarial attacks can be divided into two directions, adversarials defense and adversarially detection.
Approach:	They propose a score-based generative method to implicitly model the data distribution using a log-density distribution and supervised contrastive learning to guide the estimation using label information.
Outcome:	The proposed method improves on three text classification tasks on four advanced attack algorithms.

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Xingrun Xing, Zheng Liu, Shitao Xiao, Boyan Gao, Yiming Liang, Haokun Lin, Xianlin Zeng, Guoqi Li, Jiajun Zhang

SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture (2025.naacl-long)

Challenge:	Large language models (LLMs) driven by scaling laws can be developed in large model sizes.
Approach:	They propose a pruning-aware pretraining approach that decouples LLM pruning from direct pretraining.
Outcome:	The proposed model outperforms pretraining models with 100M 1B parameters in commen sense benchmarks.

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Jiayi Han, Liang Du, Hongwei Du, Xiangguo Zhou, Yiwen Wu, Yuanfang Zhang, Weibo Zheng, Donghong Han

Green & Sustainable NLP

Challenge:	balancing the training budget, downstream performance, and general capabilities of large language models remains a challenge in many applications.
Approach:	They propose a mixture of expert framework based on Soft LoRA and Identity Mixture . SLIM allows dynamic routing between LoRA adapters and identity layers .
Outcome:	The proposed framework reduces training cost while maintaining general capabilities . it can be open-sourced upon publication.

Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)

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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun

Information Extraction & Text Mining

Challenge:	Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities.
Approach:	They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts.
Outcome:	The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs.

Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge (2024.emnlp-main)

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Yuxuan Wang, Yueqian Wang, Pengfei Wu, Jianxin Liang, Dongyan Zhao, Yang Liu, Zilong Zheng

GRAG: Graph Retrieval-Augmented Generation (2025.findings-naacl)

Challenge:	despite advances in multimodal large language models, the challenge of interpreting long-form videos remains a challenge . despite advancements in video-language benchmarks, the inefficiency in temporal grounding and limited pre-trained context window size remains .
Approach:	They propose a framework that bootstraps MLLMs with advanced temporal grounding capabilities and broadens their contextual scope.
Outcome:	The proposed framework significantly enhances the temporal capabilities of existing MLLMs.

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Yuntong Hu, Zhihan Lei, Zheng Zhang, Bo Pan, Chen Ling, Liang Zhao

Structured Data in NLP

Challenge:	Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and is not suitable for networked documents.
Approach:	They propose a novel divide-and-conquer strategy that retrieves optimal subgraph structure in linear time.
Outcome:	The proposed approach outperforms current state-of-the-art methods on graph reasoning benchmarks.

Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization (2025.findings-emnlp)

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Zhengzhao Lai, Youbin Zheng, Zhenyang Cai, Haonan Lyu, Jingpu Yang, Hong-Qing Liang, Yan Hu, Benyou Wang

Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models (2026.eacl-long)

Challenge:	characterization imaging data is fundamental to acquiring materials information.
Approach:	a team of researchers develop a benchmark for materials characterization imaging data . the goal is to bridge this gap by addressing 1,500 questions that require expert-level expertise.
Outcome:	a new benchmark for materials characterization imaging data is presented . the benchmark reveals that MLLMs perform poorly when addressing higher-level questions .

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Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun, Yingfei Sun

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	Existing research treats MLLMs as unified systems optimized through end-to-end training, but the impact of vision encoder’s prior knowledge is seldom investigated.
Approach:	They propose a metric to quantify the effect of prior knowledge on MLLM performance by integrating prior knowledge at the vision encoder level into a training framework.
Outcome:	The proposed training framework incorporates prior knowledge at the vision encoder level, and significantly boosts visual understanding capabilities of MLLMs.

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering (2025.findings-acl)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Zheng Chu, Huiming Fan, Jingchang Chen, Qianyu Wang, Mingda Yang, Jiafeng Liang, Zhongjie Wang, Hao Li, Guo Tang, Ming Liu, Bing Qin

GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems (2020.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable reasoning capabilities, but they still face challenges in knowledge-intensive multi-hop reasoning.
Approach:	They propose a method that uses self-critique feedback to guide iterative reasoning by enabling iteration and self-evaluation of its intermediate reasoning steps.
Outcome:	The proposed method surpasses the previous SOTA by 8.6% on three multi-hop reasoning datasets.

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Lishan Huang, Zheng Ye, Jinghui Qin, Liang Lin, Xiaodan Liang

Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)

Challenge:	Existing evaluation metrics only consider surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows.
Approach:	They propose a graph-enhanced evaluation metric GRADE to evaluate dialogue coherence . GRADE incorporates utterance-level contextualized representations and fine-grained topic-level graph representations to improve communication logic.
Outcome:	The proposed evaluation metric outperforms state-of-the-art metrics on measuring diverse dialogue models in terms of Pearson and Spearman correlations with human judgments.

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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong

ACE-M3: Automatic Capability Evaluator for Multimodal Medical Models (2025.coling-main)

Challenge:	Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance.
Approach:	They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process.
Outcome:	Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models.

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Xiechi Zhang, Shunfan Zheng, Linlin Wang, Gerard de Melo, Zhu Cao, Xiaoling Wang, Liang He

SPEAK: Spiking Neurons as an Entropy-Aware Tokenizer for Large Language Models (2026.acl-long)

Challenge:	Existing metrics for multimodal large language models only focus on token overlap and may not align with human judgment.
Approach:	They propose an open-source model that assesses the question answering abilities of multimodal large language models.
Outcome:	Experiments show that the ACE-M3 model performs better than existing models and is more reliable than existing metrics.

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Ming Chen, Wenyao Li, Chao Liang, Shi Gu, Peng Lin, De Ma, Huajin Tang, Qian Zheng, Gang Pan

Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (2024.emnlp-main)

Challenge:	Existing tokenizers fail to explicitly leverage historical tokenization results . large language models (LLMs) have demonstrated remarkable effectiveness across NLP tasks .
Approach:	They propose a tokenizer that integrates spiking neurons to explicitly leverage historical tokenization results.
Outcome:	The proposed tokenizer leverages historical tokenization results, but does not selectively leverage history based on contextual relevance.

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Yongsen Zheng, Ruilin Xu, Guohua Wang, Liang Lin, Kwok-Yan Lam

ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models (2025.acl-long)

Challenge:	Existing methods to mitigate Matthew effect in offline recommendation systems are not effective . a number of studies have identified two root causes for the Matthew effect .
Approach:	They propose a framework to address the Matthew effect in conversational recommendation systems . they build hypergraphs to learn multi-level user interests to alleviate the Matthew effec .
Outcome:	The proposed framework achieves state-of-the-art performance on four CRS-based datasets . it improves on item-, entity-, word-oriented multiple-channel hypergraphs compared with existing methods .

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Xuxu Liu, Siyuan Liang, Mengya Han, Yong Luo, Aishan Liu, Xiantao Cai, Zheng He, Dacheng Tao

JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialogue Policy Learning (2024.lrec-main)

Challenge:	Existing backdoor models are limited in coverage of attack, system integrity and backdoor alignment . ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs.
Approach:	They propose a framework that allows attackers to inject backdoor through parameter efficient fine-tuning or without fine-uning techniques.
Outcome:	ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs.

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Wai-Chung Kwan, Huimin Wang, Hongru Wang, Zezhong Wang, Bin Liang, Xian Wu, Yefeng Zheng, Kam-Fai Wong

A Survey on Cross-Lingual Summarization (2022.tacl-1)

Challenge:	Dialogue policy learning (DPL) aims to determine an abstract representation (also known as action) to guide what the response should be.
Approach:	They propose a joint Transformer-based model that generates a token-grained policy that allows more dynamic dialogue action generation without the need for predefined action candidates.
Outcome:	The proposed model outperforms existing models showing improvements of 9% and 13% in success rate and 34% and 37% in diversity of dialogue actions across two benchmark dialogue modeling tasks.

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Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou

Chain of Methodologies: Scaling Test Time Computation without Training (2025.findings-acl)

Challenge:	Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language.
Approach:	They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other .
Outcome:	The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis.

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Cong Liu, Jie Wu, Weigang Wu, Xu Chen, Liang Lin, Wei-Shi Zheng

An Alignment-Agnostic Model for Chinese Text Error Correction (2021.findings-emnlp)

Challenge:	Existing prompts for complex reasoning tasks are limited to specific tasks with few-shot examples due to constraints like context length and information extraction accuracy.
Approach:	They propose a method to build structured reasoning processes by injecting human insights into LLMs' training data.
Outcome:	The proposed framework outperforms baselines in the analysis of large language models.

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Liying Zheng, Yue Deng, Weishun Song, Liang Xu, Jing Xiao

Syntactic Text Processing

Challenge:	Existing models for Chinese text error correction can correct mistaken, missing and redundant characters, but they cannot handle missing or redundant characters.
Approach:	They propose an alignment-agnostic framework to correct Chinese text errors . framework detects missing and redundant characters and can be used as a cold start model .
Outcome:	The proposed framework can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided.

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)

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Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition (2021.findings-emnlp)

Challenge:	Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies.
Approach:	They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature .
Outcome:	The proposed framework outperforms existing systems at long and short answer criteria.

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Guolin Zheng, Yubei Xiao, Ke Gong, Pan Zhou, Xiaodan Liang, Liang Lin

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)

Challenge:	Existing methods to learn the transfer from speech to text are unexplored . how to solve the representation discrepancy of speech and text is unexplorable .
Approach:	They propose a cooperative acoustic and linguistic representation learning method to fuse and utilize contextual information of speech and text.
Outcome:	The proposed method outperforms existing methods on low-resource speech recognition.

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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints.
Approach:	They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking .
Outcome:	The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints.

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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

Challenge:	Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns.
Approach:	They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users.
Outcome:	The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks.

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Learning to Stop in Structured Prediction for Neural Machine Translation (N19-1)

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Mingbo Ma, Renjie Zheng, Liang Huang

Machine Translation

Challenge:	Beam search optimization solves many problems in neural machine translation, but lacks principled stopping criteria and does not learn how to stop during training.
Approach:	They propose a ranking method which enables an optimal beam search stop-ping criteria and a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training.
Outcome:	Experiments on synthetic and real languages show that the proposed methods improve translation quality and length.

GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)

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Bo Pan, Zhen Xiong, Guanchen Wu, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao

UNIVID: Unified Vision-Language Model for Video Moderation (2026.acl-industry)

Challenge:	Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis.
Approach:	They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations.
Outcome:	Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like.

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Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao

Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency (2025.acl-long)

Challenge:	Existing video moderation systems rely on fragmented black-box classification models that are difficult to maintain and lack transparency.
Approach:	They propose a Unified Vision-Language model for Video Moderation that generates policy-aware captions that serve as an interpretable intermediate representation.
Outcome:	The proposed model reduces violation leakage and overkill rate by 42.7% while reducing maintenance costs.

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Jiafeng Liang, Shixin Jiang, Xuan Dong, Ning Wang, Zheng Chu, Hui Su, Jinlan Fu, Ming Liu, See-Kiong Ng, Bing Qin

Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation (2023.emnlp-main)

Challenge:	Large Multimodal Models (LMMs) have demonstrated impressive performance on general video comprehension benchmarks, but their robustness needs to be thoroughly investigated for broader applications.
Approach:	They propose a temporal robustness benchmark which introduces temporal inconsistency perturbations separately at the visual and textual modalities to assess the robustness of models.
Outcome:	The proposed method improves the model’s robustness and reliability in temporal analysis.

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Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang

Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)

Challenge:	Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration.
Approach:	They propose a framework that allows the model to "cheat" the objective by duplicating knowledge segments in a superficial pattern matching based on overlap.
Outcome:	The proposed framework can be applied to a WoW dataset and shows that it works across models and decoding strategies.

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Andrea Sottana, Bin Liang, Kai Zou, Zheng Yuan

Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework (2020.findings-emnlp)

Challenge:	Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement.
Approach:	They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction.
Outcome:	The proposed model outperforms many popular models according to human reviewers on the majority of metrics, while scoring much worse when using classic automatic evaluation metrics.

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Mingbo Ma, Baigong Zheng, Kaibo Liu, Renjie Zheng, Hairong Liu, Kainan Peng, Kenneth Church, Liang Huang

Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval (2025.acl-long)

Challenge:	Text-to-speech synthesis (TTS) has seen rapid progress in recent years, but still suffers from latencies.
Approach:	They propose a neural incremental TTS approach that synthesizes speech in an online fashion, playing a segment of audio while generating the next.
Outcome:	Experiments on English and Chinese TTS show that the proposed approach achieves similar speech naturalness compared to full sentence TTS, but with a constant (1-2 words) latency.

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Zheng Liu, Ze Liu, Zhengyang Liang, Junjie Zhou, Shitao Xiao, Chao Gao, Chen Jason Zhang, Defu Lian

Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation (2026.findings-acl)

Challenge:	Existing multimodal retrieval models are lacking in visual representations of multimodal data.
Approach:	They propose a visualized information retrieval paradigm where multimodal information is represented by a unified visual format called Screenshots for various retrieval applications.
Outcome:	The proposed model is based on a large dataset of screenshots from diverse sources . it is compared with existing models and lays a solid foundation for the new model .

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Jinwen Chen, Hainan Zhang, Liang Pang, Yongxin Tong, Haibo Zhou, Wei Lin, Zhiming Zheng

LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain (2025.findings-naacl)

Challenge:	Existing RAG systems require uploading local documents to the cloud, resulting in inference latency and poor generalization on out-of-distribution (OOD) inputs.
Approach:	They propose a generalizable knowledge-distilled parametric RAG model aligned with standard RAG in document structure and parameter activation.
Outcome:	The proposed model outperforms baselines in accuracy and generalizes well on out-of-distribution (OOD) data.

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Joel Niklaus, Lucia Zheng, Arya D. McCarthy, Christopher Hahn, Brian M Rosen, Peter Henderson, Daniel E. Ho, Garrett Honke, Percy Liang, Christopher D Manning

MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document (2023.findings-emnlp)

Challenge:	In general, instruction tuning is important for direct user interaction, but the legal domain is underrepresented in typical instruction datasets.
Approach:	They aggregate 58 annotated legal datasets and write instructions for each to create LawInstruct.
Outcome:	The proposed model improves on LegalBench across all model sizes, but no drop in MMLU.

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Zheng Chu, Zekun Wang, Jiafeng Liang, Ming Liu, Bing Qin

Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision (2026.acl-long)

Challenge:	Existing work models time implicitly, making it difficult to handle complex relationships . a novel temporal reasoning framework explicitly models the temporal relationships among facts by multi-view temporal graphs .
Approach:	They propose a multi-view temporal graph-based temporal reasoning framework that explicitly models the temporal relationships among facts by multi-visit temporal charts.
Outcome:	The proposed framework gives more consistent answers under question perturbations.

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Ge Chang, Jinbo Su, Jiacheng Liu, Pengfei Yang, Yuhao Shang, Huiwen Zheng, Hongli Ma, Yan Liang, Yuanchun Li, Yunxin Liu