Papers by Zhoujun Li

Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization (2023.findings-emnlp)

Challenge:	Existing studies focus on language-agnostic settings, neglecting the inherently multilingual nature of modern software development.
Approach:	They propose a proportion-dependent scaling law that prioritizes high-utility languages . they propose PLs to have varying effects during pre-training that affect model performance .
Outcome:	The proposed scaling law is based on 1000+ experiments across multiple languages and models.

Copied to clipboard

Weixiao Zhou, Gengyao Li, Xianfu Cheng, Xinnian Liang, Junnan Zhu, Feifei Zhai, Zhoujun Li

CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation (2022.findings-emnlp)

Challenge:	Existing methods for dialogue summarization only apply to specific scenarios and domains.
Approach:	They propose a pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization.
Outcome:	The proposed model significantly outperforms state-of-the-art models on three dialogue summarization datasets from different scenarios and domains.

Copied to clipboard

Jian Yang, Shaohan Huang, Shuming Ma, Yuwei Yin, Li Dong, Dongdong Zhang, Hongcheng Guo, Zhoujun Li, Furu Wei

Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation (2022.acl-long)

Challenge:	Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data.
Approach:	They propose a cross-lingual entity projection framework to enable zero-shot cross-linguistic NER with the help of a multilingual labeled sequence translation model.
Outcome:	The proposed method outperforms the baseline method on two benchmarks by a large margin of +3 7 F1 scores and achieves state-of-the-art performance.

Copied to clipboard

Xinyu Pi, Bing Wang, Yan Gao, Jiaqi Guo, Zhoujun Li, Jian-Guang Lou

CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)

Challenge:	Existing Text-to-SQL parsers are vulnerable to perturbations in NL questions . we propose the Adversarial Table Perturbation (ATP) as a new attacking paradigm .
Approach:	They propose to use the Adversarial Table Perturbation to measure robustness of Text-to-SQL parsers against adversarial perturbations.
Outcome:	The proposed approach outperforms baseline methods in robustness evaluations on ADVETA and can be used in future projects.

Copied to clipboard

Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin

IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)

Challenge:	Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences.
Approach:	They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks.
Outcome:	The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones.

Copied to clipboard

Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li

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

Challenge:	Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion.
Approach:	They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree .
Outcome:	The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work.

Copied to clipboard

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

Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)

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.

Copied to clipboard

Wei Zhang, Jian Yang, Jiaxi Yang, Ya Wang, Zhoujun Li, Zeyu Cui, Binyuan Hui, Junyang Lin

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality (2023.eacl-main)

Challenge:	Code large language models (LLMs) enhance programming by understanding and generating code across languages.
Approach:	a new benchmark evaluates code understanding and generation in repositories using code large language models.
Outcome:	The proposed model improves code understanding and generation in repositories by evaluating 1,888 test cases across 6 programming languages.

Copied to clipboard

Xinnian Liang, Shuangzhi Wu, Chenhao Cui, Jiaqi Bai, Chao Bian, Zhoujun Li

Dialogue Systems & Conversational Agents

Challenge:	Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM.
Approach:	They propose a topic-aware global-local centrality model to help select the salient context from all sub-topics.
Outcome:	The proposed model outperforms baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM.

PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)

Copied to clipboard

Juncheng Wan, Jian Yang, Shuming Ma, Dongdong Zhang, Weinan Zhang, Yong Yu, Zhoujun Li

Multilingual Agreement for Multilingual Neural Machine Translation (2021.acl-short)

Challenge:	Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure.
Approach:	They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy.
Outcome:	The proposed method improves translation performance and robustness to noise on three benchmarks.

Copied to clipboard

Jian Yang, Yuwei Yin, Shuming Ma, Haoyang Huang, Dongdong Zhang, Zhoujun Li, Furu Wei

TableBank: Table Benchmark for Image-based Table Detection and Recognition (2020.lrec-1)

Challenge:	Existing models that only use auxiliary languages to encourage multilingual agreement ignore the relationships between different language pairs.
Approach:	They propose a multilingual agreement-based method which explicitly models the agreement between different translation directions by randomly substituting some fragments of the source language with their counterpart translations of auxiliary languages.
Outcome:	The proposed method improves on the multilingual translation task of 10 language pairs.

Copied to clipboard

Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, Zhoujun Li

Smart-Start Decoding for Neural Machine Translation (2021.naacl-main)

Challenge:	Existing techniques for table detection and recognition are limited to document types and layouts.
Approach:	They propose to build a table detection and recognition dataset with weak supervision from Word and Latex documents on the internet.
Outcome:	The proposed dataset contains 417K high quality labeled tables and is publicly available.

Copied to clipboard

Jian Yang, Shuming Ma, Dongdong Zhang, Juncheng Wan, Zhoujun Li, Ming Zhou

Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)

Challenge:	Existing neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to left.
Approach:	They propose a method that starts decoding target words from the right side of a median word and generates words on the left.
Outcome:	The proposed method outperforms baseline models on three datasets.

Copied to clipboard

Bo Zhang, Xiaoming Zhang, Yun Liu, Lei Cheng, Zhoujun Li

New Intent Discovery with Attracting and Dispersing Prototype (2024.lrec-main)

Challenge:	Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains.
Approach:	They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data.
Outcome:	The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification.

Copied to clipboard

Shun Zhang, Jian Yang, Jiaqi Bai, Chaoran Yan, Tongliang Li, Zhao Yan, Zhoujun Li

Text Clustering

Challenge:	Existing methods for detecting new intents with labeled data are not cluster-friendly . a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype .
Approach:	They propose a robust and adaptive prototypical learning framework for globally distinct decision boundaries for both known and new intent categories.
Outcome:	The proposed method improves on CLINC, BANKING, and StackOverflow benchmarks on three challenging benchmarks.

GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator (2023.acl-long)

Copied to clipboard

Jian Yang, Shuming Ma, Li Dong, Shaohan Huang, Haoyang Huang, Yuwei Yin, Dongdong Zhang, Liqun Yang, Furu Wei, Zhoujun Li

Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction (2021.findings-acl)

Challenge:	Existing pre-training methods underutilize the benefits of language understanding for generation.
Approach:	They propose a GAN-style model for encoder-decoder pre-training with an auxiliary discriminator.
Outcome:	The proposed model outperforms existing pre-trained models and achieves state-of-the-art performance.

Copied to clipboard

Tianyang Zhao, Zhao Yan, Yunbo Cao, Zhoujun Li

Information Extraction & Text Mining

Challenge:	Existing methods for identifying relations from dialogues do not fully consider the particularity of dialogues, making them difficult to understand the semantics between conversational arguments.
Approach:	They propose two tasks to enhance the extraction of dialogue-based relations . speaker prediction captures the characteristics of speakerrelated entities . the trigger words prediction provides supportive contexts for relations between arguments .
Outcome:	The proposed tasks improve the extraction of dialogue-based relations . speaker prediction captures the characteristics of speakerrelated entities . the trigger words prediction provides supportive contexts for relations between arguments .

TWT: Table with Written Text for Controlled Data-to-Text Generation (2021.findings-emnlp)

Copied to clipboard

Tongliang Li, Lei Fang, Jian-Guang Lou, Zhoujun Li

Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (D19-1)

Challenge:	Existing methods output hallucinated text that is not faithful on TWT.
Approach:	They propose to generate text conditioned on the structured data and a prefix by leveraging pre-trained neural models.
Outcome:	The proposed approach outperforms state-of-the-art methods under automatic and human evaluation metrics.

Copied to clipboard

Ze Yang, Can Xu, Wei Wu, Zhoujun Li

C-ICL: Contrastive In-context Learning for Information Extraction (2024.findings-emnlp)

Challenge:	Existing methods for news comment generation have not been well studied.
Approach:	They propose a “read-attend-comment” procedure for automatic news comment generation and formalize it with a reading network and a generation network.
Outcome:	The proposed procedure outperforms existing methods in terms of automatic evaluation and human judgment on two public datasets.

Copied to clipboard

Ying Mo, Jiahao Liu, Jian Yang, Qifan Wang, Shun Zhang, Jingang Wang, Zhoujun Li

Low-Resource NLP

Challenge:	Existing methods for in-context learning with large language models focus on using correct or negative examples, ignoring the potential value of incorrect or negative samples.
Approach:	They propose a few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations.
Outcome:	The proposed technique outperforms previous few-shot in-context learning methods on a broad spectrum of related tasks.

M2C: Towards Automatic Multimodal Manga Complement (2023.findings-emnlp)

Copied to clipboard

Hongcheng Guo, Boyang Wang, Jiaqi Bai, Jiaheng Liu, Jian Yang, Zhoujun Li

PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models (2022.emnlp-main)

Challenge:	Multimodal manga analysis focuses on enhancing manga understanding with visual and textual features.
Approach:	They propose a task to enhance manga understanding with visual and textual features by providing a shared semantic space for vision and language understanding.
Outcome:	The proposed task provides a shared semantic space for vision and language understanding.

Copied to clipboard

Yupeng Zhang, Hongzhi Zhang, Sirui Wang, Wei Wu, Zhoujun Li

DependEval: Benchmarking LLMs for Repository Dependency Understanding (2025.findings-acl)

Challenge:	A wide range of NLP tasks benefit from fine-tuning of pretrained language models (PLMs), however, a number of redundant parameters which contribute less to the downstream task are observed in a directly fine- tuned model.
Approach:	They propose a noisy training mechanism which considers each parameter’s importance in the downstream task to help fine-tune pretrained language models.
Outcome:	The proposed method can be used to fine-tune pretrained language models on a wide range of tasks and consistently achieve higher performance.

Copied to clipboard

Junjia Du, Yadi Liu, Hongcheng Guo, Jiawei Wang, Haojian Huang, Yunyi Ni, Zhoujun Li

Challenge:	a benchmark is designed to evaluate the repository-level dependency understanding of large language models (LLMs) based on 2683 repositories from real-world websites.
Approach:	They propose a benchmark to evaluate repository dependency understanding for large language models . DEPENDEVAL evaluates models on three core tasks across 8 programming languages .
Outcome:	The benchmark evaluates models on three core tasks across 8 programming languages from real-world repositories.

Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)

Copied to clipboard

Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions.
Approach:	They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps.
Outcome:	The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%.

Copied to clipboard

Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

Towards Modeling Role-Aware Centrality for Dialogue Summarization (2022.aacl-short)

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

Copied to clipboard

Xinnian Liang, Chao Bian, Shuangzhi Wu, Zhoujun Li

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for dialogue summarization consider roles separately where interactions among different roles are not fully explored.
Approach:	They propose a novel role-aware centrality model to capture role interactions by involving role prompts to control what kind of summary to generate.
Outcome:	The proposed model achieves state-of-the-art on two public benchmark datasets, CSDS and MC.

Improving Neural Machine Translation with Soft Template Prediction (2020.acl-main)

Copied to clipboard

Jian Yang, Shuming Ma, Dongdong Zhang, Zhoujun Li, Ming Zhou

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

Challenge:	Recent advances in neural machine translation (NMT) depend on source text to generate translation.
Approach:	They propose to use extracted templates from tree structures as soft target templates to guide the translation procedure.
Outcome:	The proposed model outperforms baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.

Copied to clipboard

Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

Copied to clipboard

Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, Ming Zhou

Challenge:	Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information.
Approach:	They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation .
Outcome:	The proposed model trains on DocBank accurately recognize layout information for a variety of documents.

Improving Unsupervised Extractive Summarization with Facet-Aware Modeling (2021.findings-acl)

Copied to clipboard

Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li

Information Extraction & Text Mining

Challenge:	Existing extractive summarization methods tend to select sentences within the same facet, which leads to facet bias.
Approach:	They propose a facet-aware centrality-based ranking model that gives a weight to the sentence centrality score.
Outcome:	The proposed method outperforms baseline models on a wide range of summarization tasks and performs comparably to other models.

Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)

Copied to clipboard

Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li

Modeling Multi-Granularity Hierarchical Features for Relation Extraction (2022.naacl-main)

Challenge:	Existing work performs code repair and commit message generation independently.
Approach:	They propose a cascaded method to repair program codes and generate commit messages in a unified framework.
Outcome:	The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset.

Copied to clipboard

Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li

Information Extraction & Text Mining

Challenge:	Existing work on relation extraction focuses on constructing explicit structured features using knowledge graph and dependency tree.
Approach:	They propose a method to extract multi-granularity features based solely on the original input sentences.
Outcome:	The proposed method outperforms state-of-the-art models that even use external knowledge on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred Revisited.

CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers (2022.findings-emnlp)

Copied to clipboard

Dongling Xiao, LinZheng Chai, Qian-Wen Zhang, Zhao Yan, Zhoujun Li, Yunbo Cao

Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots (P18-2)

Challenge:	Existing text-to-SQL methods focus on making full use of history context, but neglect to explicitly comprehend the schema and conversational dependency.
Approach:	They propose a CQR-SQL that explicitly exploits schema and conversational dependency for multi-turn SQL parsing.
Outcome:	The proposed method exploits schema and contextual dependency for multi-turn SQL parsing.

Copied to clipboard

Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou

Information Retrieval

Challenge:	Existing methods to learn matching models for retrieval-based chatbots are lacking.
Approach:	They propose a method that uses a sequence-to-sequence architecture model as a weak annotator to judge the matching degree of unlabeled pairs and performs learning with both the weak signals and the unlabed data.
Outcome:	The proposed method improves on two public data sets on matching models on retrieval-based chatbots.

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation (2022.emnlp-main)

Copied to clipboard

Hongcheng Guo, Jiaheng Liu, Haoyang Huang, Jian Yang, Zhoujun Li, Dongdong Zhang, Zheng Cui

Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context (2021.emnlp-main)

Challenge:	Recent advances struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world.
Approach:	They propose to train different MMT models to support translations between different languages.
Outcome:	The proposed model is able to handle the above issues by providing a shared semantic space for multiple languages.

Copied to clipboard

Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li

Keyphrase Generation with Correlation Constraints (D18-1)

Challenge:	Embedding based methods are widely used for unsupervised keyphrase extraction tasks.
Approach:	They propose a method where local and global contexts are jointly modeled.
Outcome:	The proposed method outperforms most models while generalizing better on input documents with different domains and length.

Copied to clipboard

Jun Chen, Xiaoming Zhang, Yu Wu, Zhao Yan, Zhoujun Li

Know What I don’t Know: Handling Ambiguous and Unknown Questions for Text-to-SQL (2023.findings-acl)

Challenge:	Existing methods for keyphrase generation ignore correlation among keyphrases, resulting in duplication and coverage issues.
Approach:	They propose a new sequence-to-sequence architecture for keyphrase generation that captures correlation among keyphrases by preceding phrases to eliminate duplicate phrases and improve result coherence.
Outcome:	The proposed model outperforms the state-of-the-art method on benchmark datasets in terms of accuracy and diversity.

Copied to clipboard

Bing Wang, Yan Gao, Zhoujun Li, Jian-Guang Lou

Low-Resource Response Generation with Template Prior (D19-1)

Challenge:	Existing text-to-SQL parsers generate a plausible SQL query for arbitrary user questions, thereby failing to handle problematic user questions.
Approach:	They propose a weakly supervised DTE model for error detection, localization, and explanation.
Outcome:	The proposed model achieves the best result on real-world examples and generated examples compared with baselines.

Copied to clipboard

Ze Yang, Wei Wu, Jian Yang, Can Xu, Zhoujun Li

ResLoRA: Identity Residual Mapping in Low-Rank Adaption (2024.findings-acl)

Challenge:	Existing open domain response generation models are limited to paired data, but are less explored in real-world applications.
Approach:	They propose to train a neural response generation model with unpaired data and paired data as prior.
Outcome:	The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available.

Copied to clipboard

Shuhua Shi, Shaohan Huang, Minghui Song, Zhoujun Li, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

Challenge:	Low-rank adaptation (LoRA) is one of the most popular parameter-efficient fine-tuning methods.
Approach:	They propose a low-rank adaptation method that adds residual paths during training and merges them together during inference to achieve better results.
Outcome:	The proposed method achieves 2.5x faster convergence speed and improves performance by 14.3% on NLG, NLU, and text-to-image tasks.

M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)

Copied to clipboard

Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li

Structured Data in NLP

Challenge:	Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis.
Approach:	They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources.
Outcome:	Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages.

m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt (2024.lrec-main)

Copied to clipboard

Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai, Bing Wang, Jiaheng Liu, Xinnian Liang, LinZheng Chai, Liqun Yang, Zhoujun Li

UniCoder: Scaling Code Large Language Model via Universal Code (2024.acl-long)

Challenge:	Existing multimodal neural machine translation models focus on bilingual translation, but experimental results show that they outperform the text-only baselines and multilingual multimodal methods by a large margin.
Approach:	They propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual Neural Machine Translation (m3P) this framework aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation.
Outcome:	The proposed framework outperforms previous text-only baselines and multilingual multimodal methods by a large margin.

Copied to clipboard

Tao Sun, Linzheng Chai, Jian Yang, Yuwei Yin, Hongcheng Guo, Jiaheng Liu, Bing Wang, Liqun Yang, Zhoujun Li

Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)

Challenge:	Experimental results show that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin.
Approach:	They introduce the universal code (UniCode) as the intermediate representation of algorithm steps using conventions of programming languages.
Outcome:	The proposed model outperforms previous prompting methods by a large margin . the proposed model is based on a dataset of natural-language questions and code solutions .

Copied to clipboard

Yunli Wang, Yu Wu, Lili Mou, Zhoujun Li, Wenhan Chao

Formality Style Transfer with Shared Latent Space (2020.coling-main)

Challenge:	Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way.
Approach:	They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora.
Outcome:	The proposed method can be used to generate a state-of-the-art on a small dataset.

Copied to clipboard

Yunli Wang, Yu Wu, Lili Mou, Zhoujun Li, WenHan Chao

Towards Multi-System Log Anomaly Detection (2025.acl-industry)

Challenge:	Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora.
Approach:	They propose a new approach for formality style transfer using shared latent space and two auxiliary losses.
Outcome:	The proposed approach outperforms baselines in various settings, especially when limited data is available.

Copied to clipboard

Boyang Wang, Runqiang Zang, Hongcheng Guo, Shun Zhang, Shaosheng Cao, Donglin Di, Zhoujun Li

Representation Learning

Challenge:	Existing models require dataset-specific training, causing costly procedures and performance bottlenecks.
Approach:	They propose a log anomaly detection model with semantic relational reasoning that extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors.
Outcome:	The proposed model extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors.

UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)

Copied to clipboard

Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li

Towards Real-world Scenario: Imbalanced New Intent Discovery (2024.acl-long)

Challenge:	Existing methods overlook the challenge of effectively transforming structure information from NL to SQL.
Approach:	They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL.
Outcome:	The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes.

Copied to clipboard

Shun Zhang, Yan Chaoran, Jian Yang, Jiaheng Liu, Ying Mo, Jiaqi Bai, Tongliang Li, Zhoujun Li

mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture (2024.findings-emnlp)

Challenge:	Existing studies focus on detecting known and previously undefined categories of user intent . skewed and long-tailed distributions often encountered in open-world scenarios .
Approach:	They propose to use imbalanced new intent discovery task to identify familiar and novel intent categories within long-tailed distributions.
Outcome:	The proposed model outperforms the existing benchmark on three datasets to simulate the real-world long-tail distributions.

Copied to clipboard

Wei Zhang, Hongcheng Guo, Jian Yang, Zhoujin Tian, Yi Zhang, Yan Chaoran, Zhoujun Li, Tongliang Li, Xu Shi, Liangfan Zheng, Bo Zhang

Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)

Challenge:	Root cause analysis (RCA) in Micro-services architectures with escalating complexity is challenging due to fault propagation and circular dependencies among nodes.
Approach:	They propose a framework where multiple agents follow Agent Workflow and collaborate in blockchain-inspired voting to ensure the reliability of root cause analysis.
Outcome:	The proposed framework reduces the number of steps and standardizes task processing through Agent Workflow.

Copied to clipboard

Haoyan Liu, Lei Fang, Qian Liu, Bei Chen, Jian-Guang Lou, Zhoujun Li

API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs (2023.emnlp-main)

Challenge:	Existing models for text-to-SQL do not explicitly introduce common knowledge to address comparison relations.
Approach:	They propose to leverage adjective-noun phrasing knowledge mined from the web to predict comparison relations in text-to-SQL.
Outcome:	The proposed approach improves on the original and re-split Spider datasets on comparison relation prediction.

Copied to clipboard

Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, Yongbin Li

Natural Language Interfaces

Challenge:	Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools.
Approach:	They propose a runnable evaluation system consisting of 73 API tools and an annotation system for 314 tool-use dialogues with 753 API calls.
Outcome:	The proposed benchmark assesses the effectiveness of existing LLMs by analyzing 314 tool-use dialogues with 753 API calls.

StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)

Copied to clipboard

Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, Zhoujun Li

UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models (2026.findings-acl)

Challenge:	Existing methods for generating responses following a desired style are lacking of parallel data for training.
Approach:	They propose a KL loss and a style classifier to fine-tune response generation . they show that their model can significantly outperform state-of-the-art methods .
Outcome:	The proposed model outperforms state-of-the-art models in style consistency and contextual coherence with two public datasets.

Copied to clipboard

Jiajun Wu, Jian Yang, Wei Zhang, Linzheng Chai, Yuchi Ma, Ensheng Shi, Yuqing Ma, Zhoujun Li, Xianglong Liu

MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL (2025.coling-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks, but their effectiveness relies on supervised training with extensive labeled data and computational resources.
Approach:	They propose an unsupervised method that leverages Internal Probing of Large language models for Code generation without any external corpus, even unlabeled code snippets.
Outcome:	The proposed method can achieve competitive performance compared to supervised approaches while reducing the dependency on labeled data and computational resources.

Copied to clipboard

Bing Wang, Changyu Ren, Jian Yang, Xinnian Liang, Jiaqi Bai, LinZheng Chai, Zhao Yan, Qian-Wen Zhang, Di Yin, Xing Sun, Zhoujun Li

An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks (2022.coling-1)

Challenge:	Recent LLM-based Text-to-SQL methods suffer from performance degradation on “huge” databases and complex user questions that require multi-step reasoning.
Approach:	They propose a framework that integrates a decomposer agent and auxiliary agents to generate SQL queries from natural language text.
Outcome:	The proposed framework achieves comparable execution accuracy on SQL-Llama tasks compared to the baseline model.

Copied to clipboard

Xinnian Liang, Jing Li, Shuangzhi Wu, Jiali Zeng, Yufan Jiang, Mu Li, Zhoujun Li

TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis (2025.findings-emnlp)

Challenge:	Existing unsupervised summarization methods fail to consider efficiency and effectiveness when the input document is extremely long.
Approach:	They propose an efficient Coarse-to-Fine Facet-Aware Ranking framework for unsupervised long document summarization based on the semantic block.
Outcome:	The proposed framework can achieve new state-of-the-art unsupervised summarization results on Gov-Report, billSum, arXiv, and PubMed.

Copied to clipboard

Xiang Li, Xianfu Cheng, Dezhuang Miao, Xiaoming Zhang, Zhoujun Li

CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation (2025.acl-industry)

Challenge:	Existing Transformer-based methods with missing modalities are difficult to use and have quadratic complexity.
Approach:	They propose a text-enhanced Fusion Mamba framework for robust MSA with missing modalities . a Text-aware Modality Enhancement module aligns and enriches non-text modality while reconstructing missing text semantics.
Outcome:	The proposed method is efficient under missing modalities and can be used in long-range modeling and multimodal fusion scenarios.

Copied to clipboard

Kaiwen Yan, Hongcheng Guo, Xuanqing Shi, Shaosheng Cao, Donglin Di, Zhoujun Li

TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization (2022.findings-naacl)

Challenge:	CodeIF assesses the ability of large language models to adhere to task-oriented instructions in code generation tasks.
Approach:	They introduce a benchmark designed to assess LLMs' ability to adhere to task-oriented instructions within diverse code generation scenarios.
Outcome:	The proposed benchmark assesses LLMs' ability to adhere to task-oriented instructions in code generation tasks across a wide range of complexity levels and programming domains.

Copied to clipboard

Ze Yang, Christian Wang, Zhoujin Tian, Wei Wu, Zhoujun Li