Papers by Meng Sun

Is Word Segmentation Necessary for Deep Learning of Chinese Representations? (P19-1)

Challenge:	Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining.
Approach:	They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead.
Outcome:	The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes.

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Xiaoya Li, Yuxian Meng, Xiaofei Sun, Qinghong Han, Arianna Yuan, Jiwei Li

Representation Learning

Challenge:	Using word-based models, we compare word-oriented models with char-based ones . word-driven models are more vulnerable to data sparsity and the presence of out-of-vocabulary words .
Approach:	They benchmark word-based models with char-based model which does not involve word segmentation in four NLP benchmark tasks.
Outcome:	The proposed model outperforms char-based models in four NLP benchmark tasks.

Dependency Parsing as MRC-based Span-Span Prediction (2022.acl-long)

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Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu, Jiwei Li

Syntactic Text Processing

Challenge:	Existing methods for dependency parsing address the issue that edges should be constructed at the text span/subtree level rather than word level.
Approach:	They propose a method that constructs dependency trees by directly modeling span-span relations by modeling subtree-subtree relationships.
Outcome:	The proposed method constructs dependency trees by modeling span-span relations . it can retrieve missing spans in the span proposal stage, which leads to higher recall .

Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias (2023.findings-acl)

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Zhiyuan Zhang, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)

Challenge:	Existing methods to fine-tune pre-trained language models (PLMs) are not safe, since the fine-uning process is invisible to the user.
Approach:	They propose a technique to study the dynamic process of fine-tuning for finding poisonous dimensions using diffusion theory.
Outcome:	The proposed approach can detect poisonous dimensions with abnormal dynamics, purify them and fine-tune them on a clean dataset.

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Qingyu Meng, Min Chen, Dingming Liu, Yifan Mo, Yue Su, Xin Sun, Koen Hindriks, Jiahuan Pei

Dialogue Systems & Conversational Agents

Challenge:	Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change.
Approach:	They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes .
Outcome:	The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation.

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)

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Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo

Information Extraction & Text Mining

Challenge:	Existing solutions for quotation extraction use rule-based approaches and sequence labeling models.
Approach:	They propose a Context and Former-Label Enhanced Net for quotation extraction.
Outcome:	The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset.

Exploring Conditional Variational Mechanism to Pinyin Input Method for Addressing One-to-Many Mappings in Low-Resource Scenarios (2024.acl-short)

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Bin Sun, Jianfeng Li, Hao Zhou, Fandong Meng, Kan Li, Jie Zhou

Responsible & Trustworthy NLP

Challenge:	Experimental results demonstrate the superior performance of our method.
Approach:	They propose to leverage conditional variational mechanism to simplify pinyin IME . they employ a strategy that facilitates interaction between pinyan and Chinese character information .
Outcome:	The proposed method improves the performance of pinyin input method engine (IME) under low-resource conditions.

ConRPG: Paraphrase Generation using Contexts as Regularizer (2021.emnlp-main)

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Yuxian Meng, Xiang Ao, Qing He, Xiaofei Sun, Qinghong Han, Fei Wu, Chun Fan, Jiwei Li

PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension (2025.acl-long)

Challenge:	Existing methods for paraphrase generation lack reliable supervision signals.
Approach:	They propose an unsupervised paradigm for paraphrase generation based on contextual language models, candidate filtering and paraphrase model training based upon the selected candidates.
Outcome:	The proposed paradigm outperforms existing paraphrase generation methods in supervised and unsupervised setups.

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Kun Ouyang, Yuanxin Liu, Shicheng Li, Yi Liu, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)

Challenge:	Existing benchmarks on punchline comprehension suffer from language shortcuts that allow models to rely on text, lack of question diversity, and narrow focus on a specific domain of multimodal content.
Approach:	They propose a multimodal punchline comprehension benchmark to assess models' ability to comprehend punchlines.
Outcome:	The proposed model surpasses in-context learning and chain-of-thought in punchline comprehension.

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Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, Jin Xu

Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)

Challenge:	Recent studies show that pre-trained masked language models can be factual knowledge bases.
Approach:	They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a .
Outcome:	The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases .

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Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, Maosong Sun

Robustness in NLP

Challenge:	Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms.
Approach:	They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately.
Outcome:	The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods.

LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)

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Zengkui Sun, Yijin Liu, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

Cross-Lingual Knowledge Editing in Large Language Models (2024.acl-long)

Challenge:	Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue.
Approach:	They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder.
Outcome:	The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets.

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Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu, Fandong Meng

Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (2024.lrec-main)

Challenge:	Knowledge editing is a promising technique to adapt large language models to new knowledge without retraining from scratch.
Approach:	They propose to use a multilingual dataset to translate a large-scale cross-lingual synthetic dataset from English to Chinese and then to evaluate their performance in Chinese.
Outcome:	The proposed method can change model performance on several special cases without retraining from scratch.

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Libo Sun, Siyuan Wang, Meng Han, Ruofei Lai, Xinyu Zhang, Xuanjing Huang, Zhongyu Wei

MultiCodeAttack: Iterative Jailbreak Attacking on LLMs with Multi-Code Prompt Injection (2026.findings-acl)

Challenge:	Product review summarization aims to generate a concise summary based on product reviews . factual accuracy, aspect comprehensiveness, and content relevance are challenges .
Approach:	They propose an FB-Thinker framework to improve product review summarization ability . they propose two Chinese product review summary datasets for instruction-tuning and evaluation .
Outcome:	The proposed framework improves product review summarization with forward reasoning and backward refinement.

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Weifeng Sun, Meng Yan, Zhou Yang, Yuchen Chen, Song Sun, David Lo

Responsible & Trustworthy NLP

Challenge:	Existing approaches to jailbreak rely on fixed template design and a single programming language . however, existing approaches do not consider language diversity or adaptive template evolution .
Approach:	They propose a structured jailbreak framework that explores and optimizes multi-language code templates.
Outcome:	The proposed framework outperforms existing jailbreak baselines and produces higher harmful outputs than baseline methods.

An MRC Framework for Semantic Role Labeling (2022.coling-1)

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Nan Wang, Jiwei Li, Yuxian Meng, Xiaofei Sun, Han Qiu, Ziyao Wang, Guoyin Wang, Jun He

Semantic Parsing

Challenge:	Existing work on semantic role labels ignores the semantic connection between the two tasks . et al. (2010) defined two types of semantic roles: core roles and non-core roles.
Approach:	They propose to use machine reading comprehension to bridge the gap between these two tasks . they formalize predicate disambiguation as multiple-choice machine reading understanding .
Outcome:	The proposed framework achieves state-of-the-art or comparable results to previous work . it uses the descriptions of candidate senses of a given predicate as options to select the correct sense .

Outdated Issue Aware Decoding for Factual Knowledge Editing (2024.findings-acl)

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Zengkui Sun, Yijin Liu, Jiaan Wang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

Reasoning

Challenge:	Existing knowledge editing methods retain outdated responses for reasoning questions . naively retraining LLMs can be computationally intensive and can lead to catastrophic forgetting .
Approach:	They propose a simple yet effective decoding strategy to enhance edited models on reasoning questions.
Outcome:	The proposed method outDates ISsue aware deCOding (DISCO) to improve models on reasoning questions.

kFolden: k-Fold Ensemble for Out-Of-Distribution Detection (2021.emnlp-main)

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Xiaoya Li, Jiwei Li, Xiaofei Sun, Chun Fan, Tianwei Zhang, Fei Wu, Yuxian Meng, Jun Zhang

Text Classification

Challenge:	Existing studies studying OOD detection in NLP often rely on external data to diversify model predictions.
Approach:	They propose a framework which mimics OOD detection behavior without external data . they take text classification as an archetype and compare them to existing datasets .
Outcome:	The proposed framework can resolve in- and out-distribution examples in a natural way using existing datasets.

Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence (2024.emnlp-main)

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Junru Lu, Jiazheng Li, Siyu An, Meng Zhao, Yulan He, Di Yin, Xing Sun

Challenge:	Existing studies attributed verbosity to biased labels, but new research shows that DPO can be effective in mitigating verboses.
Approach:	They propose to use a method to reduce the amount of verbosity in LLMs by using a downsampling approach.
Outcome:	The proposed approach overcomes the problem of verbosity by reducing the length reliance of the proposed algorithm.

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu

Enhancing Byzantine-Resistant Aggregations with Client Embedding (2024.findings-emnlp)

Challenge:	Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos .
Approach:	They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs.
Outcome:	The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss.

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Zhiyuan Zhang, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Robustness in NLP

Challenge:	Existing Byzantine-resistant aggregations detect poisonous clients but cannot defend against backdoor injection by malicious attackers in natural language tasks.
Approach:	They propose to embed client parameters to enhance Byzantine-resistant aggregations.
Outcome:	The proposed client embeddings detect poisonous clients and discard them . the proposed algorithms can't defend against backdoor injection by malicious attackers in natural language tasks .

WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent (2026.findings-acl)

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Lingfeng Zhang, Yongan Sun, Jinpeng Hu, Hui Ma, Ying Yang, Kuien Liu, Zenglin Shi, Meng Wang

Reasoning

Challenge:	Existing web agents struggle with complex tasks due to rigid planning strategies and hallucination-prone reasoning.
Approach:	They propose a task-uncertainty-driven Adaptive Planning Mechanism that adaptively selects planning modes to navigate unknown environments.
Outcome:	The proposed framework performs better on the WebArena and WebVoyager benchmarks than existing frameworks.

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)

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Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang

Challenge:	Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes.
Approach:	They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks.
Outcome:	The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.

Fast Nearest Neighbor Machine Translation (2022.findings-acl)

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Yuxian Meng, Xiaoya Li, Xiayu Zheng, Fei Wu, Xiaofei Sun, Tianwei Zhang, Jiwei Li

Machine Translation

Challenge:	Fast kNN-MT uses the entire corpus as the datastore for the nearest neighbor search . knn-MT is two-orders slower than vanilla MT models .
Approach:	They propose a fast kNN-MT model that uses the entire corpus as the datastore for nearest neighbor search.
Outcome:	The proposed model is two-orders faster than kNN-MT and is only two times slower than the standard model.

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)

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Zhiyu Xu, Lean Wang, Yuanxin Liu, Lei Li, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)

Challenge:	Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection.
Approach:	They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM.
Outcome:	The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead.

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

Challenge:	Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal.
Approach:	They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations.
Outcome:	The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses.

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

Information Extraction & Text Mining

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome:	The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.

CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts (2022.findings-emnlp)

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Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng

Low-Resource NLP

Challenge:	Comparative opinion classification is a common linguistic phenomenon.
Approach:	They propose a framework for comparative opinion classification using embedded knowledge in pre-trained language models.
Outcome:	The proposed framework delivers state-of-the-art and robust performance on all benchmark datasets.

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information (2022.findings-acl)

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Fanchao Qi, Chuancheng Lv, Zhiyuan Liu, Xiaojun Meng, Maosong Sun, Hai-Tao Zheng

From Shijing to English and German: Resources and Evaluation for LLM Translation of Early Chinese Poetry (2026.findings-acl)

Challenge:	Existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes.
Approach:	They propose to build a multilingual sememe KB based on a dictionary called BabelNet . they use multilingual synonyms, multilingual glosses and images to encode sememes .
Outcome:	The proposed model outperforms previous methods in terms of MAP and F1 scores.

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Ying Jiao, Meng Sun

Multilinguality

Challenge:	Large language models (LLMs) show promise in literary translation, but their performance in poetry remains unexplored.
Approach:	They propose a framework that integrates knowledge-driven, rule-based, and LLM-as-judge metrics into a Shijing corpus . their code, lexical KB, and corpus reconstruction protocols are available at https://github.com/ML-KULeuven/ShijingLLMTrans.
Outcome:	The proposed framework achieves higher human correlation than traditional metrics and high statistical stability.

An Empirical Study of Many-to-Many Summarization with Large Language Models (2025.acl-long)

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Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang, Yuxuan Cao, Jiarong Xu, Haoxiang Shi, Jie Zhou

Pre-training with Meta Learning for Chinese Word Segmentation (2021.naacl-main)

Challenge:	Recent studies have shown that large language models (LLMs) have strong multilingual abilities, giving them the potential to perform M2MS in real applications.
Approach:	They propose to use many-to-many summarization (M2MS) to generate a brief summary in any language given a document also in any other language.
Outcome:	The proposed model outperforms zero-shot LLMs in terms of automatic evaluations.

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Zhen Ke, Liang Shi, Songtao Sun, Erli Meng, Bin Wang, Xipeng Qiu

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)

Challenge:	Recent studies show that pre-trained models are beneficial to Chinese Word Segmentation (CWS). However, these models lack task-specific prior segmentation knowledge.
Approach:	They propose a pre-trained Chinese word segmentation model MetaSeg which incorporates meta learning into a multi-criteria pre-training task.
Outcome:	Empirical results show that MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin

ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information (2021.acl-long)

Challenge:	Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization.
Approach:	They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question.
Outcome:	The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks.

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Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu, Jiwei Li

Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)

Challenge:	ChineseBERT model incorporates glyph and pinyin information of Chinese characters into pretraining . proposed model achieves new performance boost over baseline models with fewer training steps .
Approach:	They propose a ChineseBERT model that incorporates glyph and pinyin information into pretraining . the glyph embedding is obtained based on different fonts of a character, and the pinyink embeddment characterizes the pronunciation of Chinese characters.
Outcome:	The proposed model achieves new performance boosts over baseline models with fewer training steps.

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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu

Paraphrase Generation as Unsupervised Machine Translation (2022.coling-1)

Challenge:	Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain.
Approach:	They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words.
Outcome:	The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.

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Xiaofei Sun, Yufei Tian, Yuxian Meng, Nanyun Peng, Fei Wu, Jiwei Li, Chun Fan

BertGCN: Transductive Text Classification by Combining GNN and BERT (2021.findings-acl)

Challenge:	Existing methods for paraphrase generation rely on labeled datasets or are limited in narrow domains.
Approach:	They propose a paradigm for paraphrase generation by treating the task as unsupervised machine translation based on pairs of unlabeled monolingual sentences.
Outcome:	The proposed paradigm can generate paraphrases on a large unlabeled monolingual corpus without relying on bilingual sentence pairs.

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Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, Fei Wu

Information Retrieval

Challenge:	Text classification is a core task in natural language processing (NLP) Graph neural networks (GNNs) serve as an effective approach for transductive learning.
Approach:	They propose a model that combines large scale pretraining and transductive learning for text classification.
Outcome:	The proposed model achieves SOTA performance on a wide range of datasets.

Markovian Linguistic-Temporal Bridge: Unlocking the Potential of LLMs for Time Series Forecasting (2026.acl-long)

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Siming Sun, Kai Zhang, Xuejun Jiang, Wenchao Meng, Qinmin Yang

Challenge:	Pretrained Large Language Models (LLMs) are based on token-level linguistic-temporal alignment, leading to stacking of logically disjointed tokens as input.
Approach:	They propose a framework that distills latent evolutionary patterns of language into a Markovian state transition graph, which is transferred as a structural prior to the time series domain.
Outcome:	The proposed framework achieves global structural isomorphism between the linguistic and temporal domains.

Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning (2026.findings-acl)

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Xinyan Guan, Jiali Zeng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Fandong Meng

Reasoning

Challenge:	Large language models generate costly yet semantically void reasoning on beyond-capability tasks . the dominant failure mode is specious reasoning, superficially valid outputs with subtle hallucinations .
Approach:	They propose a capability-aligned reinforcement learning approach that aligns model behavior with capability boundaries.
Outcome:	The proposed model reduces futile reasoning while maintaining performance across tasks.

Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning (2023.emnlp-main)

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Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment (2023.findings-emnlp)

Challenge:	In-context learning (ICL) is a promising capability for large language models (LLMs) but its underlying mechanism remains unexplored.
Approach:	They propose a demonstration compression technique to expedite inference and an analysis framework for diagnosing ICL errors in GPT2-XL.
Outcome:	The proposed method improves ICL performance and expedites inference.

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin

Natural Language Interfaces

Challenge:	Existing studies on dialogue quality assessment are uncapable of providing an end-to-end and human-epistemic assessment dataset . open-domain dialogue assessment is complicated and costly, but it can be done by recruiting human evaluators.
Approach:	They propose a large-scale dialogue quality assessment dataset for automatically assessing open-domain dialogue quality.
Outcome:	The proposed dataset is openly accessible at https://github.com/yukunZhao/Dialogue_quality_evaluation.

Joint Optimization of Training Data and Policy in RLHF (2026.findings-acl)

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Zhuohao Yu, Jiali Zeng, Weizheng Gu, Mengyuan Sun, Yidong Wang, Fandong Meng, Jie Zhou, Shikun Zhang, Wei Ye

Challenge:	JODP optimizes policies on fixed training inputs, limiting the diversity of learning signals.
Approach:	They propose a framework where policy generates improved variants of training problems to enhance its own learning.
Outcome:	The proposed framework improves on safety alignment tasks by allowing 4B models to reach 8B model performance with less than 1% additional computational overhead.

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

Speech & Audio in NLP

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

Seg2Act: Global Context-aware Action Generation for Document Logical Structuring (2024.emnlp-main)

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Zichao Li, Shaojie He, Meng Liao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Yanxiong Lu, Xianpei Han, Le Sun

Challenge:	Document logical structuring is crucial for document intelligence due to the complexity of text segment dependencies in the document.
Approach:	They propose an end-to-end, generation-based method for document logical structuring that generates the action sequence via a global context-aware generative model and updates its global context and current logical structure based on the generated actions.
Outcome:	Experiments on ChCatExt and HierDoc datasets show that Seg2Act performs better than previous methods in both supervised and transfer learning settings.

MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter (2024.acl-long)

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Jitai Hao, Weiwei Sun, Xin Xin, Qi Meng, Zhumin Chen, Pengjie Ren, Zhaochun Ren

From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)

Challenge:	Parameter-Efficient Fine-tuning (PEFT) methods are limited on knowledge-intensive tasks due to the limited number of trainable parameters.
Approach:	They propose a mechanism that fine-tunes Large Language Models with larger adapters . they store and update the parameters of larger adapter adapters on the CPU .
Outcome:	The proposed method achieves comparable results to those obtained with larger memory capacities over the limited bandwidth of PCI Express (PCIe).

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Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu

Information Extraction & Text Mining

Challenge:	Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events.
Approach:	They propose a knowledge projection paradigm for event relation extraction using commonalities between events.
Outcome:	The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations.

Layer-wise Model Pruning based on Mutual Information (2021.emnlp-main)

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Chun Fan, Jiwei Li, Tianwei Zhang, Xiang Ao, Fei Wu, Yuxian Meng, Xiaofei Sun

WebDP: Understanding Discourse Structures in Semi-Structured Web Documents (2023.findings-acl)

Challenge:	In spite of impressive results of neural networks, the huge model size has hindered their applications in cases where computation and memory resources are limited.
Approach:	They propose a method for layer-wise pruning using mutual information based feature selection in SVMs and logistic regression.
Outcome:	The proposed pruning strategy offers greater speedup and higher performance than weight-based pruning methods.

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Peilin Liu, Hongyu Lin, Meng Liao, Hao Xiang, Xianpei Han, Le Sun

Xiaofei Sun, Yuxian Meng, Xiang Ao, Fei Wu, Tianwei Zhang, Jiwei Li, Chun Fan

Challenge:	Web documents are one of the most primary and biggest data resources in current era, and understanding their discourse structure will benefit various downstream document processing applications.
Approach:	They propose a web document discourse structure representation schema by extending classical discourse theories and adding special features to well represent discourse characteristics of web documents.
Outcome:	The proposed task is feasible but challenging for current models.

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Low-Resource NLP

Challenge:	Existing methods to measure sentence similarity face limited dataset size and training-test gap . existing methods lack large-scale labeled datasets with labeles that are labor-intensive and expensive .
Approach:	They propose a framework that measures sentence similarity by comparing probabilities of generating two sentences given the same context.
Outcome:	The proposed framework achieves significant performance boosts over baselines under supervised and unsupervised settings.

CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)

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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao

Dice Loss for Data-imbalanced NLP Tasks (2020.acl-main)

Challenge:	Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios.
Approach:	They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions.
Outcome:	The proposed framework improves generalization and realism of large language models under complex and irregular table conditions.

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Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, Jiwei Li

Syntactic Text Processing

Challenge:	Using dice loss, we find that data imbalance is a common issue in many NLP tasks . data imbalance affects the performance of many tasks, such as tagging and machine reading comprehension .
Approach:	They propose to use dice loss to replace the standard cross-entropy objective for data-imbalanced NLP tasks.
Outcome:	The proposed training objective achieves significant performance boost on a wide range of data imbalanced tasks.

A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict (2022.findings-naacl)

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Yiyi Liu, Yequan Wang, Aixin Sun, Xuying Meng, Jing Li, Jiafeng Guo

Sentiment Analysis

Challenge:	Sarcasm employs ambivalence, where one says something positive but actually means negative . linguistically, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit .
Approach:	They propose a Dual-Channel Framework to model literal and implied sentiments separately . they propose sarcastic networks that can detect sarcasm sentiments in political debates .
Outcome:	The proposed framework achieves state-of-the-art on political debates and Twitter datasets.

COLD: A Benchmark for Chinese Offensive Language Detection (2022.emnlp-main)

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Jiawen Deng, Jingyan Zhou, Hao Sun, Chujie Zheng, Fei Mi, Helen Meng, Minlie Huang

Ethical NLP

Challenge:	Offensive language detection is crucial for maintaining a civilized social media platform and deploying pre-trained language models.
Approach:	They propose a benchmark benchmark for Chinese offensive language analysis including a Chinese Offensive Language Dataset and a baseline detector which is trained on the dataset.
Outcome:	The proposed benchmark contributes to Chinese offensive language detection which is challenging for existing resources.

From Attack Surfaces to Actual Operations: A Survey of Modern LLM Jailbreaks (2026.findings-acl)

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Ruikang Zhou, Changsheng Sun, Mark Huasong Meng

Robustness in NLP

Challenge:	Existing taxonomies focus on manipulation methods rather than underlying mechanisms, limiting our understanding of attack effectiveness and defensive strategies.
Approach:	They propose a two-fold taxonomy to categorize attacks across three tiers based on exploited vulnerabilities and approaches and an operational taxonomies to evaluate attacks across four dimensions.
Outcome:	The proposed taxonomy categorizes attacks across three tiers based on exploited vulnerabilities and approaches and evaluates attacks on four dimensions to assess real-world feasibility and sustainability.

ECO v1: Towards Event-Centric Opinion Mining (2022.findings-acl)

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Ruoxi Xu, Hongyu Lin, Meng Liao, Xianpei Han, Jin Xu, Wei Tan, Yingfei Sun, Le Sun

Sentiment Analysis

Challenge:	Existing studies on event-centric opinion mining focus on entity-centric opinions . entity-centered opinions focus on sentimental polarity of events, while event-centered ones focus on content .
Approach:	They propose to perform event-centric opinion mining on event-argument structure and expression categorizing theory and benchmark it against a pioneer corpus.
Outcome:	The proposed task is feasible and challenging, and the results are beneficial for future studies.

Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries (2022.coling-1)

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Xiaofei Sun, Zijun Sun, Yuxian Meng, Jiwei Li, Chun Fan

CORES: Code-Oriented Reasoning for Complex Text-to-SQL and Generalizable TableQA (2026.findings-acl)

Challenge:	Existing models focus on local word prediction, and cannot make high level plans on what to generate.
Approach:	They propose a pipelined system that summarises, outlines and elaborates on each bullet point to generate the corresponding segment.
Outcome:	The proposed system produces long texts with significantly better quality and faster convergence speed.

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Meng Zhang, Ruochun Jin, Yuanxi Peng, Wenjing Yang, Haotian Wang, Liting Sun, Kun Hu, Silin Yang, Zhang Ke-di

Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension (C18-1)

Challenge:	Text-to-SQL models struggle with complex analytical tasks such as generating simple SQL queries.
Approach:	They propose a text-to-sql model that leverages Python as a procedural reasoning pivot to enhance both complex SQL generation and tabular reasoning.
Outcome:	The proposed model outperforms baseline models on six text-to-SQL benchmarks by 6.44% on average while maintaining good capability on three tableQA benchmarks.

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Liang Wang, Sujian Li, Wei Zhao, Kewei Shen, Meng Sun, Ruoyu Jia, Jingming Liu

Responsible & Trustworthy NLP

Challenge:	Recent studies have shown that cloze-style reading comprehension is a popular task for measuring the progress of natural language understanding.
Approach:	They propose a multi-perspective framework which can be seen as joint training of heterogeneous experts and aggregate context information from different perspectives.
Outcome:	The proposed framework achieves new state-of-the-art over previous strong baselines on a recently released cloze-test dataset.

Proofread: Fixes All Errors with One Tap (2024.acl-demos)

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Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai