Papers by Quan Wang

Weight Distillation: Transferring the Knowledge in Neural Network Parameters (2021.acl-long)

Challenge:	Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved.
Approach:	They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion.
Outcome:	The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0.

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Ye Lin, Yanyang Li, Ziyang Wang, Bei Li, Quan Du, Tong Xiao, Jingbo Zhu

AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression (2023.acl-long)

Challenge:	Knowledge distillation is an effective method for model acceleration and compression.
Approach:	They propose to use parameters to distill knowledge from large neural networks to small ones . they propose to do this by using a parameter generator to transfer the knowledge to a small neural network .
Outcome:	The proposed method learns a small network 1.88 2.94x faster than the large network but with competitive BLEU points.

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Siyue Wu, Hongzhan Chen, Xiaojun Quan, Qifan Wang, Rui Wang

Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness (2024.emnlp-main)

Challenge:	Existing knowledge distillation methods focus on the transfer of model-specific knowledge but overlook data-specific information.
Approach:	They propose an attribution-driven knowledge distillation approach which explores the token-level rationale behind the teacher model and transfers attribution knowledge to the student model.
Outcome:	The proposed method outperforms state-of-the-art methods on the GLUE benchmark and shows that it is more efficient than existing methods.

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Shixuan Ma, Quan Wang

Feature-Adaptive and Data-Scalable In-Context Learning (2024.acl-long)

Challenge:	Existing zero-shot detection paradigms that use token cohesiveness are not available for large language models.
Approach:	They propose a generic dual-channel detection paradigm that uses token cohesiveness as a plug-and-play module to improve existing zero-shot detectors.
Outcome:	The proposed model is able to detect human-like text in black-box environments.

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Jiahao Li, Quan Wang, Licheng Zhang, Guoqing Jin, Zhendong Mao

E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)

Challenge:	In-context learning (ICL) is a popular way to stimulate LLM capabilities for downstream tasks due to context length constraints.
Approach:	They propose a feature-adaptive and data-scalable in-context learning framework which leverages task-adaptives to promote inference on the downstream task.
Outcome:	The proposed framework outperforms state-of-the-art methods on 10 datasets under different data settings and LLM scale.

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Fengyi Fu, Lei Zhang, Quan Wang, Zhendong Mao

Improving Knowledge Graph Embedding Using Simple Constraints (P18-1)

Challenge:	Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities.
Approach:	They propose a framework that integrates emotion correlation learning, utilization, and supervising.
Outcome:	The proposed framework improves empathetic perception and expression on a humanized dialogue dataset.

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Boyang Ding, Quan Wang, Bin Wang, Li Guo

A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)

Challenge:	Recent efforts focused on designing more complicated models or incorporating extra information beyond triples.
Approach:	They propose to use non-negativity constraints on entity representations and approximate entailment constraints on relation representations to improve KG embedding.
Outcome:	The proposed model outperforms baseline models on WordNet, Freebase, and DBpedia.

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Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, Shujian Huang, Tong Xiao, Jingbo Zhu

MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (2022.acl-long)

Challenge:	Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs.
Approach:	They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary .
Outcome:	The proposed method achieves 13.64 55.53% accuracy between English and four distant languages.

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Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen, Rui Yan

IDEATE: Detecting AI-Generated Text Using Internal and External Factual Structures (2024.lrec-main)

Challenge:	Existing methods for emotional support conversation are too coarse-grained to capture user’s instant mental state and focus on expressing empathy in the response rather than gradually reducing user’ s distress.
Approach:	They propose a model which firstly infers the user’s fine-grained emotional status and then responds skillfully using a mixture of strategy.
Outcome:	The proposed model infers the user’s fine-grained emotional status and responds skillfully using mixed-up strategy modeling.

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Quan Wang, Licheng Zhang, Zikang Guo, Zhendong Mao

Improving Preference Alignment of LLM with Inference-Free Self-Refinement (2025.findings-emnlp)

Challenge:	Existing methods to detect AI-generated text rely on internal evidences, but external evidences are not considered.
Approach:	They propose a hierarchical graph network that utilizes internal and external factual structures to detect AI-generated text.
Outcome:	The proposed network outperforms current state-of-the-art methods on four datasets.

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Fukun Ma, Kaibin Tian, Jieting Xue, Xiaoyi Wang, Ye Ma, Quan Chen, Peng Jiang, Lijie Wen

Challenge:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Approach:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Outcome:	Experiments show that incorporating IFSR into preference alignment yields performance improvement over 10%.

Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity (2026.acl-long)

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Shixuan Ma, Jiahao Li, Zhendong Mao, Quan Wang

Text Style Transfer with Contrastive Transfer Pattern Mining (2023.acl-long)

Challenge:	Existing methods for detecting LLM-generated text rely on statistical features that are insufficient for reliable detection.
Approach:	They propose a temperature-sensitive detector that modulates decoding temperature and monitors how probability distributions respond to temperature.
Outcome:	The proposed method is based on a temperature sensitivity feature and a simple zero-shot detector built upon normalized temperature sensitivity.

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Jingxuan Han, Quan Wang, Licheng Zhang, Weidong Chen, Yan Song, Zhendong Mao

Low-Resource Generation of Multi-hop Reasoning Questions (2020.acl-main)

Challenge:	Existing methods for text style transfer only focus on the transformation between styles, yet they do not take into account that this transformation can be achieved via different hidden transfer patterns.
Approach:	They propose a novel approach which automatically mines hidden transfer patterns to improve TST . they use a clustering module to automatically discover hidden transfer pattern from the data .
Outcome:	The proposed method can be applied in a plug-and-play manner to enhance other methods to further improve their performance.

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Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin

Low-Resource NLP

Challenge:	Existing methods to generate valid and fluent questions from text are limited and insufficient for training.
Approach:	They propose to generate multi-hop reasoning questions from the raw text in a low resource circumstance by deducing over multiple relations on several sentences in the text.
Outcome:	The proposed model can be applied to the task of machine reading comprehension and achieve significant performance improvements.

Curriculum Learning for Natural Language Understanding (2020.acl-main)

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Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, Yongdong Zhang

S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction (2023.acl-long)

Challenge:	Pre-trained language models can be fine tuned to perform NLU tasks in a straightforward manner.
Approach:	They propose a pretrain-finetune paradigm for natural language understanding (NLU) they propose 'a cross-trainset' approach that allows users to distinguish easy from difficult examples .
Outcome:	The proposed approach achieves significant performance improvements on a wide range of NLU tasks.

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Benfeng Xu, Quan Wang, Yajuan Lyu, Dai Dai, Yongdong Zhang, Zhendong Mao

Information Extraction & Text Mining

Challenge:	Existing methods for relation extraction suffer from the inadequacy of large-scale annotated data.
Approach:	They propose a framework for two-stage self-training with synthetic data for relation extraction .
Outcome:	The proposed framework is based on two-stage self-training with synthetic data . it is able to synthesize large quantities of training data and iteratively and alternately learn from synthetic and golden data together.

Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)

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Jingxuan Han, Zhendong Mao, Yi Liu, Yexuan Che, Zheren Fu, Quan Wang

Information Retrieval

Challenge:	Existing studies rely on semantic similarity to retrieve knowledge but ignore fine-grained information within documents.
Approach:	They propose a fine-grained knowledge enhancement method to fill knowledge gaps with retrieved external information by a Chain-of-Thought prompting procedure and a decoding enhancement strategy to constrain the document-based decoding process.
Outcome:	The proposed method can be applied in a plug-and-play manner to enhance its performance with no additional modules or training process.

Multi-modal Concept Alignment Pre-training for Generative Medical Visual Question Answering (2024.findings-acl)

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Quan Yan, Junwen Duan, Jianxin Wang

PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)

Challenge:	Medical Visual Question Answering (Med-VQA) aims to provide accurate answers to questions regarding medical images, a task particularly challenging for open-ended questions.
Approach:	They propose a multi-modal concept alignment pre-training approach for generative Med-VQA that leverages a knowledge graph sourced from medical image-caption datasets and the Unified Medical Language System.
Outcome:	The proposed approach significantly outperforms existing methods on a set of benchmark datasets and shows high efficiency and knowledge-image alignment capability.

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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo

Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)

Challenge:	Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning .
Approach:	They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models.
Outcome:	The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show .

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Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu

Information Extraction & Text Mining

Challenge:	Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles.
Approach:	They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments .
Outcome:	The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well .

Clustering-Aware Negative Sampling for Unsupervised Sentence Representation (2023.findings-acl)

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Jinghao Deng, Fanqi Wan, Tao Yang, Xiaojun Quan, Rui Wang

MUSTIE: Multimodal Structural Transformer for Web Information Extraction (2023.acl-long)

Challenge:	Using clustering-aware learning, in-batch negatives are often ignored in sentence representation learning.
Approach:	They propose a method that integrates cluster information into contrastive learning for unsupervised sentence representation learning.
Outcome:	The proposed method compares favorably with baselines on semantic textual similarity tasks.

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Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Zenglin Xu, Shaoliang Nie, Sinong Wang, Madian Khabsa, Hamed Firooz, Dongfang Liu

Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems (2023.emnlp-main)

Challenge:	Recent sequential modeling approaches focus on extracting information from textual sources while ignoring rich information from other modalities such as image and web layout.
Approach:	They propose a novel MUltimodal Structural Transformer that integrates multiple modalities for web information extraction.
Outcome:	The proposed model outperforms existing methods on WebSRC and Common Crawl benchmarks.

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Tianyuan Shi, Liangzhi Li, Zijian Lin, Tao Yang, Xiaojun Quan, Qifan Wang

Natural Language Interfaces

Challenge:	Current approaches to task-oriented dialogue systems integrate knowledge retrieval and response generation, which poses scalability challenges when dealing with extensive knowledge bases.
Approach:	They propose a retriever-generator architecture that harnesses a retrieval and a generator to generate system responses by using feedback from the generator as pseudo-labels.
Outcome:	The proposed architecture shows superior performance on three benchmark datasets.

From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks (2025.naacl-long)

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Xiaofeng Zhang, Yihao Quan, Chen Shen, Xiaosong Yuan, Shaotian Yan, Liang Xie, Wenxiao Wang, Chaochen Gu, Hao Tang, Jieping Ye

CVSS Corpus and Massively Multilingual Speech-to-Speech Translation (2022.lrec-1)

Challenge:	Large visionlanguage models (LVLMs) are a powerful visual-language reasoning tool.
Approach:	They propose to integrate attention analysis with LLaVA-CAM to determine interactions between visual representations.
Outcome:	The proposed approach can be used to determine interactions between visual representations.

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Ye Jia, Michelle Tadmor Ramanovich, Quan Wang, Heiga Zen

Joint Generator-Ranker Learning for Natural Language Generation (2023.findings-acl)

Challenge:	Existing work on speech-to-speech translation (S2ST) systems rely on text representation, but they are text-centric.
Approach:	They introduce a massively multilingual-to-English speech-tospeech translation corpus . they synthesize the translation text from the Common Voice speech corpus and CoVoST 2 into English .
Outcome:	The proposed corpus outperforms existing models on CoVoST 2 by 5.8 BLEU . the proposed model outperformed the previous state-of-the-art model without extra data .

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Weizhou Shen, Yeyun Gong, Yelong Shen, Song Wang, Xiaojun Quan, Nan Duan, Weizhu Chen

Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation (2023.emnlp-main)

Challenge:	Existing methods for text generation train the generator and ranker individually . existing methods neglect the mutual feedback that could enhance the quality of outputs .
Approach:	They propose a joint training algorithm that integrates the generator and ranker in a single framework.
Outcome:	The proposed algorithm surpasses existing methods on four public datasets across three common generation scenarios.

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Jiaang Li, Quan Wang, Yi Liu, Licheng Zhang, Zhendong Mao

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection (2023.findings-emnlp)

Challenge:	Existing approaches to learning on Knowledge Graphs (KGs) are not critical for learning on KGs.
Approach:	They propose an alternative approach to represent entities by composing entity-corresponding codewords matched from predefined small-scale codebooks.
Outcome:	The proposed approach achieves similar results to existing methods.

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Tao Yang, Tianyuan Shi, Fanqi Wan, Xiaojun Quan, Qifan Wang, Bingzhe Wu, Jiaxiang Wu

Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have demonstrated remarkable zero-shot performance across various NLP tasks.
Approach:	They propose a method which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner and prompts an LLM to rate individual items at each turn.
Outcome:	The proposed method improves the performance and robustness of the standard GPT-3.5 personality detection task on two benchmark datasets.

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Jiaang Li, Zhendong Mao, Quan Wang, Yuning Wan, Yongdong Zhang

Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)

Challenge:	Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) however, LLMs exhibit a stylistic bias when presented with mixed contexts, revealing a bottleneck in their utility.
Approach:	They propose a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts.
Outcome:	The proposed model improves RAG pipelines by 8% with negligible latency overhead.

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Guanhuan Huang, Xiaojun Quan, Qifan Wang

MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)

Challenge:	Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up .
Approach:	They propose to generate entity autoregressively before leveraging it to guide response generation.
Outcome:	Experiments on MultiWOZ 2.1 single and CAMREST show that the proposed system generates more high-quality and entity-consistent responses in an end-to-end manner.

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Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu

Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism (2026.acl-long)

Challenge:	Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data.
Approach:	They propose a prompt tuning approach to extract attributes from product information using mixed prompts.
Outcome:	The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting.

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Yuhao Shen, Tianyu Liu, Junyi Shen, Jinyang Wu, Quan Kong, Huan Li, Cong Wang

Information Retrieval

Challenge:	Parallel Speculative Decoding (PSD) has limitations due to speedup limits and high computational waste . a novel synchronous mechanism solves the Retrieval Precision-Efficiency Dilemma .
Approach:	They propose a framework that combines a draft-verification-based approach with a synchronous mechanism to solve the Retrieval Precision-Efficiency Dilemma.
Outcome:	The proposed framework breaks speedup limits for Speculative Decoding by overlapping draft generation with verification.

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao

Challenge:	Existing methods focus on knowledge and linguistic patterns of characters.
Approach:	They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome:	The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.

XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)

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Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, Dawei Song

Adaptive Convolution for Multi-Relational Learning (N19-1)

Challenge:	Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Approach:	They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Outcome:	The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales.

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Xiaotian Jiang, Quan Wang, Bin Wang

Green & Sustainable NLP

Challenge:	Existing convolutional neural networks fail to model full interactions between entities and relations, which limits the performance of link prediction.
Approach:	They propose a convolutional network that maximizes entity-relation interactions in a convergent fashion.
Outcome:	The proposed convolutional network performs better than baseline models on multiple datasets.

DMSD: Dual-Modal Semantic Disentanglement for Compositional Zero-Shot Learning (2026.findings-acl)

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Pan Yang, Jing Yang, Ruan Xiao li, Yuling Chen, Yuankai Wu, Quan Zhou, Xu Wang

EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)

Challenge:	Compositional Zero-Shot Learning (CZSL) is a new research paradigm that learns sub-concepts from seen compositions and recognizes unseen novel combinations.
Approach:	They propose a Dual-Modal Semantic Disentanglement framework that integrates visual and textual information to achieve effective sub-concept disentangling.
Outcome:	The proposed framework achieves state-of-the-art performance on three benchmark datasets . it integrates a class-centroid bridge module to guide class centroids toward the textual space .

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Benfeng Xu, Quan Wang, Yajuan Lyu, Yabing Shi, Yong Zhu, Jie Gao, Zhendong Mao

Information Extraction & Text Mining

Challenge:	Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction.
Approach:	They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples.
Outcome:	The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction.

ReAlign: Structured Revision for Small Language Model Alignment (2025.findings-emnlp)

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Ruijun Chen, Jiajian Guo, Hongzhan Chen, Fanqi Wan, Qifan Wang, Xiaojun Quan

Disentangled Phonetic Representation for Chinese Spelling Correction (2023.acl-long)

Challenge:	weak policies struggle to generate informative on-policy samples and suffer from unstable gradients when trained on off-police signals from stronger models.
Approach:	They propose a training framework that combines stability of on-policy learning with reviser-assisted supervision.
Outcome:	The proposed training framework outperforms strong preference optimization baselines on AlpacaEval-2 and Arena-Hard.

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Zihong Liang, Xiaojun Quan, Qifan Wang

M-RangeDetector: Enhancing Generalization in Machine-Generated Text Detection through Multi-Range Attention Masks (2025.findings-acl)

Challenge:	Chinese Spelling Correction (CSC) aims to detect and correct erroneous characters in Chinese sentences.
Approach:	They propose to integrate phonetic and character representations to allow interaction between textual and phonetic information.
Outcome:	The proposed method is superior to other methods on three benchmarks.

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Kaijie Jiao, Quan Wang, Licheng Zhang, Zikang Guo, Zhendong Mao

APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models (2023.emnlp-main)

Challenge:	Existing supervised methods for text detection are overfitting within their training domains.
Approach:	They propose a method that integrates four distinct attention masking strategies into a Multi-Range Attention module to learn various writing strategies for machine-generated text detection.
Outcome:	The proposed method improves the generalization capability of existing detectors on three datasets.

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Qifan Wang, Yuning Mao, Jingang Wang, Hanchao Yu, Shaoliang Nie, Sinong Wang, Fuli Feng, Lifu Huang, Xiaojun Quan, Zenglin Xu, Dongfang Liu

RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)

Challenge:	Existing prompt tuning methods only introduce prompts at the input layer, limiting performance and leaving large room for improvement.
Approach:	They propose a method that involves tuning a small set of soft prompts for pre-trained language models.
Outcome:	The proposed method outperforms state-of-the-art methods with pre-trained models on the SuperGLUE benchmark.

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Guofeng Quan, Wenfeng Feng, Chuzhan Hao, Guochao Jiang, Yuewei Zhang, Hao Henry Wang

Information Retrieval

Challenge:	Existing methods for generating draft tokens rely on lightweight draft models or additional model structures to generate tokens and retrieve context from databases.
Approach:	They propose to use a pruning method to enhance model-based speculative decoding by combining the best-fit model with the best retrieval tree.
Outcome:	The proposed method achieves state-of-the-art inference acceleration across tasks such as DocQA, Summary, Code, and In-Domain QA.

CodeRipple: Wavelet-Based Detection of LLM-Generated Code (2026.acl-long)

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Xingyu Yao, Zhendong Mao, Quan Wang

Programming Languages in NLP

Challenge:	Existing training-free detectors rely on global statistics of the Token Perplexity Sequence (TPS) and struggle with code.
Approach:	They propose a training-free detection framework that characterizes TPS morphology across scales.
Outcome:	The proposed framework outperforms existing training-free detectors on three challenging benchmarks spanning programming languages, multiple generating LLMs, and various evasion strategies.

Multi-choice Relational Reasoning for Machine Reading Comprehension (2020.coling-main)

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Wuya Chen, Xiaojun Quan, Chunyu Kit, Zhengcheng Min, Jiahai Wang

Reasoning

Challenge:	cloze-style reading comprehension is a task that requires much semantic understanding and reasoning using various clues from texts.
Approach:	They propose a multi-choice relational reasoning model that emulates human reading comprehension by combining fusion representations of document, query and candidates.
Outcome:	The proposed model outperforms baseline models significantly on four datasets.

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability (2025.findings-acl)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Chiwei Zhu, Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Zhendong Mao

Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation (2024.acl-long)

Challenge:	Existing studies have shown that training language models with rationales augmentation is beneficial, but this view does not hold consistently.
Approach:	They conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance and a novel perspective of model reliability.
Outcome:	The proposed method outperforms untrained models in several areas and provides informative regulations on the broad utilization of rationales.

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Tianqi Zhong, Zhaoyi Li, Quan Wang, Linqi Song, Ying Wei, Defu Lian, Zhendong Mao

On the Calibration of Large Language Models and Alignment (2023.findings-emnlp)

Challenge:	Existing MCTG methods face a noticeable performance drop in compositional testing.
Approach:	They propose a benchmark to evaluate compositional generalization of MCTG methods by combining multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol.
Outcome:	The proposed framework improves compositional generalization performance by 3.64% and 94.4% in compositional testing.

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Chiwei Zhu, Benfeng Xu, Quan Wang, Yongdong Zhang, Zhendong Mao

Grammatical Error Correction via Mixed-Grained Weighted Training (2023.findings-emnlp)

Challenge:	Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure .
Approach:	They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training.
Outcome:	The results shed light on whether popular large language models are well-calibrated and how the training process influences model calibration.

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Jiahao Li, Quan Wang, Chiwei Zhu, Zhendong Mao, Yongdong Zhang

Link Prediction on N-ary Relational Facts: A Graph-based Approach (2021.findings-acl)

Challenge:	Empirical evaluation shows that MainGEC achieves consistent and significant performance improvements on two benchmark datasets.
Approach:	They propose to use mixed-grained weighted training to improve the training effect for GEC by analyzing the inherent discrepancies in annotated training data.
Outcome:	Empirical results show that the proposed method achieves significant performance improvements on two benchmark datasets.

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Quan Wang, Haifeng Wang, Yajuan Lyu, Yong Zhu

Structured Data in NLP

Challenge:	Existing work on knowledge graphs (KGs) focused on binary relations, but higher-arity relations are ubiquitous in real-world KGs.
Approach:	They propose a graph-based approach to link prediction on knowledge graphs using n-ary relational facts and edge-biased fully-connected attention.
Outcome:	The proposed approach performs substantially better than current state-of-the-art across a variety of n-ary relational benchmarks.

Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)

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Bei Li, Ziyang Wang, Hui Liu, Yufan Jiang, Quan Du, Tong Xiao, Huizhen Wang, Jingbo Zhu

Multilinguality

Challenge:	Experimental results show that deep training is 1:4 faster than training from scratch.
Approach:	They propose a shallow-to-deep training method that learns deep models by stacking shallow models.
Outcome:	The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks.

Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)

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Qifan Wang, Li Yang, Xiaojun Quan, Fuli Feng, Dongfang Liu, Zenglin Xu, Sinong Wang, Hao Ma

Alleviating Hallucinations in Large Language Models via Truthfulness-driven Rank-adaptive LoRA (2025.findings-acl)

Challenge:	Existing automatic question generation methods focus on encoding passage and answer to generate question.
Approach:	They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework.
Outcome:	The proposed approach outperforms existing methods on SQuAD and HotpotQA benchmarks.

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Jiahao Li, Zhendong Mao, Quan Wang

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)

Challenge:	Existing methods to improve truthfulness are training-free without modifying the LLM itself.
Approach:	They propose a rank-adaptive LoRA method to improve LLM truthfulness that allocates ranks according to truthfulness correlations of LLM modules.
Outcome:	The proposed method outperforms state-of-the-art methods on the LLM family and makes the performance of 7B LLMs exceed GPT-4.

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Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang

BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (P19-1)

Challenge:	Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts.
Approach:	They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy .
Outcome:	The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks.

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Kai Wang, Xiaojun Quan, Rui Wang

Multi-Domain Dialogue Acts and Response Co-Generation (2020.acl-main)

Challenge:	Abstractive summarization models are limited in size and noisy training data.
Approach:	They propose a bi-directional selective encoding with template model which leverages template from training data to softly select key information from each source article to guide its summarization process.
Outcome:	The proposed model improves the summarization performance significantly on a standard summarizing dataset.

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Kai Wang, Junfeng Tian, Rui Wang, Xiaojun Quan, Jianxing Yu

FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization (2024.emnlp-main)

Challenge:	Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation.
Approach:	They propose a neural co-generation model that generates dialogue acts and responses concurrently and preserves semantic structures of multi-domain dialogue acts.
Outcome:	The proposed model improves over state-of-the-art models in automatic and human evaluations on a large-scale dataset.

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Mingye Zhu, Yi Liu, Quan Wang, Junbo Guo, Zhendong Mao

Challenge:	Recent advances in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values.
Approach:	They propose a constrained optimization approach to detect and mitigate update regression with focal attention.
Outcome:	The proposed approach detects and mitigates update regression with focal attention while maintaining excellent overall performance.

Improve Safety Training of Large Language Models with Safety-Critical Singular Vectors Localization (2025.acl-long)

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Peijian Gu, Quan Wang, Zhendong Mao

Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation (2023.emnlp-main)

Challenge:	Recent work on safety training with modules such as low-rank adaptation (LoRA) to resist jailbreaks shows promise, but these approaches can inadvertently degrade a model’s general utility.
Approach:	They propose a plug-and-play method that locates safety-critical singular vectors within the model's parameter space and a dynamic rank number determination strategy to reduce parameter overhead.
Outcome:	The proposed method mitigates the impact of safety training on model utility by explicitly locating and leveraging safety-critical singular vectors within the model’s parameter space.

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Tianqi Zhong, Quan Wang, Jingxuan Han, Yongdong Zhang, Zhendong Mao

RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment (2026.acl-industry)

Challenge:	Controllable text generation (CTG) aims to generate text with desired attributes, but current methods lack high levels of controllability.
Approach:	They propose a lightweight decoding framework that reconstructs attribute distributions to balance the weights between attribute words and non-attribute words to generate more fluent text.
Outcome:	The proposed framework achieves state-of-the-art control performance on multiple CTG tasks.

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Yingfeng Luo, Hongyu Liu, DingYang Lin, Kaiyan Chang, Chenglong Wang, Bei Li, Quan Du, Tong Xiao, JingBo Zhu

Machine Translation

Challenge:	Existing routing strategies rely on heuristics, external predictors, or absolute quality estimation to capture whether the large model provides a worthwhile improvement over the small one.
Approach:	They propose a budget allocation problem for routing large model to large model . they propose heuristics, external predictors, or absolute quality estimation to determine the optimal signal for budgeted decisions.
Outcome:	The proposed model outperforms heuristics, quality/difficulty estimation baselines and achieves a superior quality–budget Pareto frontier.

Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering (2024.findings-acl)

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Peng Yixing, Quan Wang, Licheng Zhang, Yi Liu, Zhendong Mao

Natural Language Interfaces

Challenge:	Existing methods to answer complex questions rely on decomposition of complex questions into sub-questions . Existing approaches to decompose complex questions are limited by the original question .
Approach:	They propose a question decomposition approach to decompose semantically clear questions . they use the decomposed sub-questions to select relevant patterns as auxiliary information .
Outcome:	The proposed method achieves state-of-the-art performance on multiple datasets.

IAEval: A Comprehensive Evaluation of Instance Attribution on Natural Language Understanding (2023.findings-emnlp)

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Peijian Gu, Yaozong Shen, Lijie Wang, Quan Wang, Hua Wu, Zhendong Mao

Challenge:	Instance attribution (IA) aims to identify the training instances leading to the prediction of a test example.
Approach:	They propose a systematic and comprehensive evaluation scheme covering four significant requirements: sufficiency, completeness, stability and plausibility.
Outcome:	The proposed evaluation scheme covers four significant requirements: sufficiency, completeness, stability and plausibility.

Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020.acl-main)

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Kai Wang, Weizhou Shen, Yunyi Yang, Xiaojun Quan, Rui Wang

Sentiment Analysis

Challenge:	Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews.
Approach:	They propose a relational graph attention network to encode a tree structure for sentiment prediction.
Outcome:	The proposed approach improves the performance of the graph attention network (GAT) on the SemEval 2014 and Twitter datasets.

MCC-KD: Multi-CoT Consistent Knowledge Distillation (2023.findings-emnlp)

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Hongzhan Chen, Siyue Wu, Xiaojun Quan, Rui Wang, Ming Yan, Ji Zhang

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)

Challenge:	Large language models (LLMs) have demonstrated remarkable abilities in complex reasoning through chain of thought (CoT) prompting.
Approach:	They propose to generate multiple rationales for each question and enforce consistency among their predictions by minimizing the bidirectional KL-divergence between the answer distributions.
Outcome:	The proposed model achieves superior performance on in-distribution and commonsense reasoning benchmarks.

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An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, Sujian Li

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)

Challenge:	Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT .
Approach:	They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions.
Outcome:	The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards.

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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang

Programming Languages in NLP

Challenge:	Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows .
Approach:	They propose a repository-level evaluation benchmark to assess security of AI-generated code.
Outcome:	The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation.

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)

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Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, Yonggang Wang