Papers by Che Wang

Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs (2025.findings-emnlp)

Challenge:	Existing multi-hop retrieval of open-domain text-to-SQL tasks is not applicable due to the tendency to retrieve tables similar to those already retrieved but irrelevant to the question.
Approach:	They propose a multi-hop table retrieval with removal task to retrieve unretrieved tables from open-domain text-to-SQL databases.
Outcome:	The proposed method improves performance 5.7% over the previous state-of-the-art methods on open-domain text-to-SQL datasets.

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Che Liu, Cheng Ouyang, Zhongwei Wan, Haozhe Wang, Wenjia Bai, Rossella Arcucci

DAC: Decomposed Automation Correction for Text-to-SQL (2025.findings-emnlp)

Challenge:	Current methods for multimodal representation learning for electrocardiograms often result in suboptimal alignment of ECG signals with their corresponding text reports.
Approach:	They propose a framework to learn ECG representations by aligning ECG signals with paired free-text reports.
Outcome:	The proposed framework outperforms existing methods in zero-shot classification and linear probing tasks using 12 leads.

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)

Challenge:	Existing methods to improve text-to-SQL performance are hard to detect errors in SQL directly.
Approach:	They propose to use decomposed correction to improve text-to-SQL performance . they first detect errors based on decompose subtasks, then use it to correct them .
Outcome:	The proposed method improves text-to-SQL performance by 1.4% compared with previous methods .

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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai

Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)

Challenge:	Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge.
Approach:	They propose a recurrent inductive bias that aligns with the recursive nature of programming logic.
Outcome:	The proposed model achieves comparable performance to standard dense models with more parameters.

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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers (2022.coling-1)

Challenge:	Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation.
Approach:	They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task.
Outcome:	The proposed model improves inference speed by 2.3-2.7x times without compromising model performance.

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Baoxin Wang, Xingyi Duan, Dayong Wu, Wanxiang Che, Zhigang Chen, Guoping Hu

Syntactic Text Processing

Challenge:	Chinese text correction datasets focus on detecting and correcting Chinese spelling errors and grammatical errors.
Approach:	They propose a Chinese text correction dataset for native speakers . they manually annotated 1,500 Chinese texts written by native speakers.
Outcome:	The proposed dataset can detect and correct Chinese spelling errors and grammatical errors.

Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval (2025.acl-demo)

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Keyan Xu, Dingzirui Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation (2020.acl-main)

Challenge:	Existing text-to-SQL systems often lack retrieval capabilities for open-domain databases, requiring users to manually filter relevant databases.
Approach:	They propose to use database retrieval technology to locate the required databases in an open-domain database environment and enhance system cross-domain transferability through data augmentation methods.
Outcome:	The proposed system performs excellently in multi-turn text-to-SQL tasks, validating the proposed approach’s effectiveness.

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Jun Xu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing word-level policy models that learn dialog policy and language generation from dialog corpora often lead to degeneration issues where the utterances become ungrammatical or repetitive.
Approach:	They propose to represent prior dialog transitions as a graph and learn a CG grounded dialog policy that can foster a more coherent and controllable dialog.
Outcome:	The proposed framework is able to learn dialog policy in open-domain multi-turn conversation.

Cross-Lingual Machine Reading Comprehension (D19-1)

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Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

Machine Reading Comprehension

Challenge:	Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data.
Approach:	They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches .
Outcome:	The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems.

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes (2024.acl-long)

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Reasoning

Challenge:	Numerical reasoning is an essential ability for NLP systems to handle numeric information.
Approach:	They propose a numerical reasoning method that generates reliable reasoning processes by decomposing the answer formula and aim to train models to generate the process with synthesized data.
Outcome:	The proposed method improves on all five datasets with an average improvement of 1.8% compared with baselines and gpt-3.5-turbo.

Simple and Effective Graph-to-Graph Annotation Conversion (2022.coling-1)

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Yuxuan Wang, Zhilin Lei, Yuqiu Ji, Wanxiang Che

Structured Data in NLP

Challenge:	Existing work on graph-structured annotation conversions has focused on feature-based models which are not easily applicable to new conversions.
Approach:	They propose two graph-to-graph conversion approaches which use pseudo data and inherit parameters to guide conversions respectively.
Outcome:	The proposed approaches outperform strong baselines with higher conversion score on a graph-structured dataset and other datasets.

Adversarial Training for Machine Reading Comprehension with Virtual Embeddings (2021.starsem-1)

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Ziqing Yang, Yiming Cui, Chenglei Si, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

Reasoning

Challenge:	Neural networks are vulnerable to adversarial examples that have been mixed with certain perturbations.
Approach:	They propose a novel adversarial training method that perturbs the embedding matrix instead of word vectors to differentiate the roles of passages and questions.
Outcome:	The proposed method is effective universally and further improves the performance of MRC tasks.

MetaPrompting: Learning to Learn Better Prompts (2022.coling-1)

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Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che

Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)

Challenge:	Recent research on prompting moves from discrete tokens based "hard prompts" to continuous "soft prompts", which employ learnable vectors as pseudo prompt tokens and achieve better performance.
Approach:	They propose a generalized soft prompting method that uses model-agnostic meta-learning to find better initialization for soft prompts.
Outcome:	The proposed method improves on three datasets and brings new state-of-the-art performance.

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Shaolei Wang, Zhongyuan Wang, Wanxiang Che, Ting Liu

Low-Resource NLP

Challenge:	Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain.
Approach:	They propose an unsupervised learning paradigm which can work with unlabeled text corpora.
Outcome:	The proposed method performs better than existing supervised systems using word embeddings.

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models (2023.emnlp-main)

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Chengcheng Han, Xiaowei Du, Che Zhang, Yixin Lian, Xiang Li, Ming Gao, Baoyuan Wang

Dialogue Systems & Conversational Agents

Challenge:	Chain-of-Thought prompting has improved the reasoning capabilities of Large Language Models (LLMs) but it is ineffective or detrimental to the performance on reasoning tasks in Smaller Language Model (SLMs) with less than 10 billion parameters.
Approach:	They propose a Dialogue-guided Chain-of-Thought method to improve the reasoning capabilities of Large Language Models (LLMs) by generating intermediate reasoning steps in a dialogue format to guide the model to the final answer.
Outcome:	The proposed method can achieve significant performance gains over state-of-the-art competitors on four arithmetic reasoning datasets.

KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)

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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang

Natural Language Interfaces

Challenge:	Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications.
Approach:	They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics .
Outcome:	The proposed model performs best on KwaiChat but is not perfect in this situation.

On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)

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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu

Challenge:	Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks.
Approach:	They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments.
Outcome:	The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.

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.

Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety (2026.acl-long)

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Can Jin, Rui Wu, Tong Che, Qixin Zhang, Hongwu Peng, Jiahui Zhao, Zhenting Wang, Wenqi Wei, Ligong Han, Zhao Zhang, Yuan Cao, Ruixiang Tang, Dimitris N. Metaxas

Robustness in NLP

Challenge:	OpenAI introduces deliberative alignment (DA) to enhance safety of its o-series models, but effectiveness of this approach in open-source LLMs is understudied.
Approach:	They propose a case-augmented deliberative alignment method for large language models . they propose to use reinforcement learning on self-generated safety reasoning chains .
Outcome:	The proposed method avoids narrowly enumerated rules and allows broader adaptability.

Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)

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Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou

Discovering Dialog Structure Graph for Coherent Dialog Generation (2021.acl-long)

Challenge:	Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries.
Approach:	They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions.
Outcome:	The proposed framework improves the performance of the proposed benchmark by 28.2%.

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Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on dialog structure graphs from open-domain dialogs have limited number of dialog states and can be laborious and costly to annotate manually.
Approach:	They propose to use dialog structure graph as a model to discover hierarchical latent dialog states and their transitions from corpus to facilitate dialog management in a RL based dialog system.
Outcome:	The proposed model can discover meaningful dialog structure graph and significantly improve multi-turn coherence on two benchmark corpora.

Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling (2026.findings-acl)

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Shiyu Ji, Yixuan Wang, Yijun Liu, Qingfu Zhu, Wanxiang Che

Challenge:	Large Language Models (LLMs) have demonstrated exceptional performance in reasoning tasks, particularly in mathematics.
Approach:	They propose a dynamic self-consistency framework that integrates System 1 and System 2 reasoning to improve token efficiency and latency.
Outcome:	The proposed method outperforms existing methods, achieving up to 47% reduction in token consumption and 43% reduction in inference latency without significant performance loss.

A Compare-and-contrast Multistage Pipeline for Uncovering Financial Signals in Financial Reports (2023.acl-long)

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Jia-Huei Ju, Yu-Shiang Huang, Cheng-Wei Lin, Che Lin, Chuan-Ju Wang

Challenge:	Recent advances in natural language processing (NLP) have included attempts to efficiently and effectively comprehend lengthy financial documents.
Approach:	They propose a signal-highlighting task that analyzes relationships between financial reports . they also create and publicly release a human-annotated dataset for the task .
Outcome:	The proposed pipeline is based on a human-annotated dataset and validates its effectiveness.

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)

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Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

Reasoning

Challenge:	Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks .
Approach:	They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates .
Outcome:	The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories .

TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)

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Ziqing Yang, Yiming Cui, Zhipeng Chen, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)

Challenge:	Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference.
Approach:	They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code.
Outcome:	The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters.

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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che

LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)

Challenge:	Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths.
Approach:	They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models.
Outcome:	The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio.

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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)

Challenge:	Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks.
Approach:	They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework.
Outcome:	The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks.

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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che

Improving Grammatical Error Correction via Contextual Data Augmentation (2024.findings-acl)

Challenge:	Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits.
Approach:	They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task.
Outcome:	The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists.

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Yixuan Wang, Baoxin Wang, Yijun Liu, Qingfu Zhu, Dayong Wu, Wanxiang Che

Low-Resource NLP

Challenge:	Increasing use of synthetic data due to inconsistent error distribution and noisy labels is limiting the use of these data.
Approach:	They propose a method for augmentation of synthetic data with a more consistent error distribution.
Outcome:	The proposed method outperforms strong baselines and achieves state-of-the-art with only a few synthetic data.

A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)

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Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu

Machine Reading Comprehension

Challenge:	Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them.
Approach:	They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets.
Outcome:	The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts.

A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)

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Yuxuan Wang, Wanxiang Che, Ivan Titov, Shay B. Cohen, Zhilin Lei, Ting Liu

Robustness in NLP

Challenge:	Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans.
Approach:	They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations.
Outcome:	The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling.

Towards Conversational Recommendation over Multi-Type Dialogs (2020.acl-main)

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Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

Natural Language Interfaces

Challenge:	In recent years, there has been a significant increase in the work of conversational recommendation due to the rise of voice-based bots.
Approach:	They use a Chinese dialog dataset DuRecDial to study conversational recommendation in the context of multi-type dialogs where bots can proactively lead a conversation from a non-recommendation dialog to a recommendation dialog.
Outcome:	The proposed dataset allows to investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how interact with users for recommendation.

DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings (2021.emnlp-main)

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Che Liu, Rui Wang, Jinghua Liu, Jian Sun, Fei Huang, Luo Si

NASH: Numerically Aware Scoring Heuristic for Robust Semantic Similarity (2026.findings-acl)

Challenge:	Conventional approaches to learning sentence embeddings from dialogues employ the siamese-network for this task, but such architecture yields a large gap between training and evaluating.
Approach:	They propose a dialogue-based contrastive learning approach to learn sentence embeddings from dialogues using a siamese-network.
Outcome:	The proposed model outperforms baseline methods on three multi-turn dialogue datasets in terms of MAP and Spearman’s correlation measures.

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Yu-Shiang Huang, Yun-Yu Lee, Tzu-Hsin Chou, Che Lin, Chuan-Ju Wang

RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals (2025.emnlp-main)

Challenge:	Numerical precision is critical in financial NLP, yet embedding-based semantic similarity metrics exhibit numerical blindness.
Approach:	They propose a model-agnostic metric that decouples numerical verification from textual semantic evaluation.
Outcome:	The proposed metric improves numerical sensitivity while maintaining general semantic performance.

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Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che

Improving Language Model Reasoning with Self-motivated Learning (2024.lrec-main)

Challenge:	Recent advances in reasoning large language models (RLLMs) have significantly enhanced reasoning capabilities, leading to brilliant performance on table reasoning.
Approach:	They propose a method which performs iterative row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal.
Outcome:	Experiments show that the proposed method outperforms RLLMs on WikiTableQuestions and TableBench by 4.3% and achieves state-of-the-art results with comparable models.

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Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, Wanxiang Che

Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings (2022.emnlp-main)

Challenge:	Large-scale high-quality training data is important for improving the performance of models.
Approach:	They propose a framework that motivates the model to automatically generate rationales on existing datasets and improves the performance of reasoning through reinforcement learning.
Outcome:	The proposed model outperforms InstructGPT on multiple reasoning datasets and outperformed InstructGPT on other datasets.

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Che Liu, Rui Wang, Junfeng Jiang, Yongbin Li, Fei Huang

Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020.findings-emnlp)

Challenge:	Dialogue embeddings are a critical prerequisite for semantically understanding dialogues.
Approach:	They propose a self-guided contrastive learning approach called dial2vec that captures interaction patterns between interlocutors and leverages them to guide the learning of the embeddings corresponding to each interlocuter.
Outcome:	The proposed approach achieves 8.7, 9.0, and 13.8 points absolute improvements over the strongest baseline on the three evaluation tasks respectively.

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Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation (2025.findings-acl)

Challenge:	Existing pre-trained language models have shown tremendous improvements across various NLP tasks.
Approach:	They propose to revisit Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pretrained model series to the community.
Outcome:	The proposed model improves on RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac).

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Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao, Hui Shen, Jing Xiong, Rossella Arcucci, Huaxiu Yao, Mi Zhang

Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation (2025.emnlp-main)

Challenge:	Recent studies have focused on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise.
Approach:	They propose a Multimodal ECG Instruction Tuning framework that extends the capability of large language models (LLMs) for the task.
Outcome:	The proposed framework outperforms open-source LLMs and LLM backbones across two large-scale ECG datasets.

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Tianhao Niu, Yiming Cui, Baoxin Wang, Xiao Xu, Xin Yao, Qingfu Zhu, Dayong Wu, Shijin Wang, Wanxiang Che

Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation (2025.findings-acl)

Challenge:	Existing Chart2code-related training datasets suffer from limited scale, limited type coverage, and inadequate complexity.
Approach:	They propose to synthesize chart2code-related training datasets using web plotting code and chart images to address these challenges.
Outcome:	The proposed dataset exhibits the greatest diversity and higher complexity compared to other open-source Chart2code related datasets.

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Che Liu, Zhongwei Wan, Yuqi Wang, Hui Shen, Haozhe Wang, Kangyu Zheng, Mi Zhang, Rossella Arcucci

MEDA: Dynamic KV Cache Allocation for Efficient Multimodal Long-Context Inference (2025.naacl-long)

Challenge:	Existing work on 3D radiograph report generation focuses on 2D images, but 3D medical images provide more comprehensive diagnostic information.
Approach:	They propose a comprehensive training recipe for building high-performing VLMs for 3DRRG using a publicly available 3D CT-report dataset.
Outcome:	The proposed model achieves superior performance across different model sizes and input 3D medical image resolutions.

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Zhongwei Wan, Hui Shen, Xin Wang, Che Liu, Zheda Mai, Mi Zhang

LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference (2024.findings-emnlp)

Challenge:	Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources as their multimodal Key-Value (KV) cache grows with increasing input lengths, challenging memory and time efficiency.
Approach:	They propose a dynamic multimodal KV cache allocation strategy that dynamically allocating KV size based on attention entropy to better adapt to multimodal interactions.
Outcome:	The proposed model achieves up to 72% KV cache memory reduction and 2.82 faster decoding speeds while maintaining or enhancing performance on various multimodal tasks in a long context.

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Zhongwei Wan, Ziang Wu, Che Liu, Jinfa Huang, Zhihong Zhu, Peng Jin, Longyue Wang, Li Yuan

Visual Data in NLP

Challenge:	Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources for inference . the growth of their multimodal Key-Value (KV) cache challenges memory and time efficiency.
Approach:	They propose a fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache.
Outcome:	The proposed method reduces the multimodal KV cache size while maintaining performance comparable to a full cache.

LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction (2024.lrec-main)

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Yixuan Wang, Baoxin Wang, Yijun Liu, Dayong Wu, Wanxiang Che

Text Generation

Challenge:	Recent work using model ensemble methods based on voting can effectively mitigate over-correction and improve the precision of the GEC system.
Approach:	They propose a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble.
Outcome:	The proposed model can mitigate over-correction and improve accuracy of Chinese grammatical error correction tasks without a model ensemble.

From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)

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Haonan Bian, Yutao Qi, Rui Yang, Yuanxi Che, Jiaqian Wang, Heming Xia, Ranran Zhen

Experiential Co-Learning of Software-Developing Agents (2024.acl-long)

Challenge:	Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning.
Approach:	They propose an ontology-driven reasoning and chain framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs.
Outcome:	Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that the proposed framework achieves competitive performance while producing more interpretable reasoning chains.

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Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun

CLIPText: A New Paradigm for Zero-shot Text Classification (2023.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents.
Approach:	They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution.
Outcome:	The proposed framework enables agents to tackle unseen software-developing tasks more effectively.

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Libo Qin, Weiyun Wang, Qiguang Chen, Wanxiang Che

Information Extraction & Text Mining

Challenge:	Experimental results show that CLIP can be applied to zero-shot text classification tasks.
Approach:	They propose a CLIP model for zero-shot text classification that integrates prompt into CLIPText to better derive knowledge from CLIP.
Outcome:	The proposed model can be applied to a text-image matching problem and show that it can be used for language tasks.

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation (2021.emnlp-main)

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Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

Natural Language Interfaces

Challenge:	Existing datasets for conversational recommendation are limited to English and Chinese .
Approach:	They propose a bilingual parallel human-to-human recommendation dialog dataset . the data item is annotated in two languages, both English and Chinese .
Outcome:	The proposed dataset provides a testbed for future studies of multilingual and cross-lingual conversational recommendation.

Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing (D19-1)

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Yuxuan Wang, Wanxiang Che, Jiang Guo, Yijia Liu, Ting Liu

Exploring Hybrid Question Answering via Program-based Prompting (2024.acl-long)

Challenge:	Existing approaches to learn cross-lingual word embeddings in a contextual space are lacking.
Approach:	They propose a method to generate cross-lingual contextualized word embeddings using pre-trained BERT models by learning a linear transformation from contextual word alignments.
Outcome:	The proposed approach outperforms state-of-the-art models on zero-shot cross-lingual transfer parsing and is highly competitive with existing models.

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Qi Shi, Han Cui, Haofeng Wang, Qingfu Zhu, Wanxiang Che, Ting Liu

Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data? (2025.findings-acl)

Challenge:	Existing approaches to question answering over heterogeneous data are limited due to large scale of information and organic coupling of heterogenous data.
Approach:	They propose a program-based prompting framework for hybrid question answering tasks . it integrates various functions to perform hybrid information-seeking over data .
Outcome:	The proposed framework surpasses baseline systems and achieves the best performance under the fewshot settings.

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Che Liu, Zhongwei Wan, Haozhe Wang, Yinda Chen, Talha Qaiser, Chen Jin, Nikolay Burlutskiy, Fariba Yousefi, Rossella Arcucci

SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System (2026.eacl-long)

Challenge:	Medical Vision-Language Pretraining (MedVLP) models typically require large-scale datasets with paired, high-quality image-text data.
Approach:	They propose to generate large-scale synthetic image-text pairs using off-the-shelf generative models . they propose to isolate model and training settings, focusing entirely from the data perspective.
Outcome:	The proposed pipeline outperforms models trained on real data by 3.8% on averaged AUC on zero-shot classification tasks.

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Zijie Wang, Xinyu Yan, Che Wang, Zeng Zihao, Lei Xiao, Wei Yang Bryan Lim

Green & Sustainable NLP

Challenge:	Existing routing methods suffer from poor scalability and dependence on datasets for training . energy footprint is also considered in the decision to implement our new LLM routing framework .
Approach:	They propose a new LLM routing framework that dynamically allocates queries to the most appropriate LLM.
Outcome:	The proposed method improves data efficiency, adaptability, and routing accuracy compared to existing methods.

Consistency Regularization for Cross-Lingual Fine-Tuning (2021.acl-long)

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Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei

Multilinguality

Challenge:	Experimental results show that consistency regularization improves cross-lingual fine-tuning . pre-trained cross-linguistic models can transfer task-specific supervision from one language to the other .
Approach:	They propose to improve cross-lingual fine-tuning with consistency regularization . they use example consistency regularized to penalize prediction sensitivity to four types of data augmentations .
Outcome:	The proposed method improves cross-lingual fine-tuning across tasks . it can be generalized to other target languages without additional training .

TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack (2022.findings-emnlp)

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Zhen Yu, Xiaosen Wang, Wanxiang Che, Kun He

Robustness in NLP

Challenge:	Existing textual adversarial attacks use gradient or prediction confidence to generate adversarials, making it hard to be deployed in real-world applications.
Approach:	They propose a textual adversarial attack that randomly perturbs lots of words to craft an adversarial example.
Outcome:	The proposed attack outperforms existing hard-label attacks in terms of attack performance and adversary quality.

SPE: Symmetrical Prompt Enhancement for Fact Probing (2022.emnlp-main)

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Yiyuan Li, Tong Che, Yezhen Wang, Zhengbao Jiang, Caiming Xiong, Snigdha Chaturvedi

Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL (2024.findings-emnlp)

Challenge:	Recent work probes PLMs for the extent of factual knowledge through prompts . however, these methods do not consider symmetry of the task: object and subject prediction.
Approach:	They propose a continuous prompt-based method that leverages symmetry of the task by constructing symmetrical prompts for subject and object prediction.
Outcome:	The proposed method improves on a popular factual probing dataset on lAMA.

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Adaptive Unsupervised Self-training for Disfluency Detection (2022.coling-1)

Challenge:	Existing studies have explored selecting relevant demonstrations from a human-labeled demonstration pool, but these methods lack diversity and incur high labeling costs.
Approach:	They propose a method that iteratively fuses demonstrations to create a diverse demonstration pool based on human labeling or even from scratch with LLMs, reducing labeling costs.
Outcome:	The proposed method achieves an average improvement of 2.1% based on existing labeling and 5.5% from scratch on mainstream datasets.

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Zhongyuan Wang, Yixuan Wang, Shaolei Wang, Wanxiang Che

Dynamic Connected Networks for Chinese Spelling Check (2021.findings-acl)

Challenge:	Recent studies on disfluency detection heavily relies on human annotations, which are difficult and expensive to obtain in practice.
Approach:	They propose an unsupervised method that reweights the importance of each training example according to its grammatical feature and prediction confidence.
Outcome:	The proposed method improves 2.3 points over the current SOTA unsupervised method and is competitive with the SOTA supervised method.

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Baoxin Wang, Wanxiang Che, Dayong Wu, Shijin Wang, Guoping Hu, Ting Liu

SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models (2024.acl-long)

Challenge:	Chinese spelling check (CSC) is a task to detect and correct spelling errors in Chinese text.
Approach:	They propose a new architecture which generates Chinese characters via a Pinyin Enhanced Candidate Generator and then utilizes an attention-based network to model the dependencies between two adjacent Chinese characters.
Outcome:	The proposed method achieves state-of-the-art performance on three human-annotated datasets.

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Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che

MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering (2025.findings-emnlp)

Challenge:	Existing methods to address catastrophic forgetting and knowledge transfer in large language models (LLMs) ignore potential of aligning the two modules to effectively address catastrophic forgetting and knowledge transfers simultaneously.
Approach:	They propose a Shared Attentive Learning & Selection module to align the PET learning and selection modules to address catastrophic forgetting and knowledge transfer simultaneously.
Outcome:	Experiments on two CL benchmarks show that the proposed framework is superior when scaled to different model sizes, different model architectures and unseen tasks.

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Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che