Papers by Yunbo Cao

Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization (2023.findings-emnlp)

Challenge:	Existing evaluation frameworks that use large language models as referees are insufficient for accurately assessing their alignment with human intent.
Approach:	They propose a calibration framework to address positional bias in large language models as evaluators by manually annotating the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicun A Benchmark’s question prompt.
Outcome:	The proposed framework alleviates evaluation bias, resulting in closer alignment with human judgments.

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Shoujie Tong, Heming Xia, Damai Dai, Runxin Xu, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui

Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)

Challenge:	Pretrained language models can be fine-tuned on limited training data, which can overfit and thus diminish performance.
Approach:	They propose a fine-tuning strategy that selectively updates model parameters using gradients from various sub-nets dynamically generated by dropout.
Outcome:	The proposed method outperforms existing methods on the GLUE benchmark and exhibits excellent generalization ability and robustness for domain transfer, data imbalance, and low-resource scenarios.

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Linhai Zhang, Xuemeng Hu, Boyu Wang, Deyu Zhou, Qian-Wen Zhang, Yunbo Cao

Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding (2022.findings-emnlp)

Challenge:	Recent studies have shown that using external knowledge such as pre-trained word embeddings or pre-train language models only achieved limited performance improvements but with huge computational overhead.
Approach:	They propose to incorporate external knowledge into neural topic modeling by pre-trained word embeddings (PWEs) or pre-train language models (PLMs) they propose to fine-tune the neural topic model on the target dataset and reduce the huge size of training data.
Outcome:	The proposed approach outperforms current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs on three datasets and greatly reduces the huge size of training data.

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Jiali Zeng, Yongjing Yin, Yufan Jiang, Shuangzhi Wu, Yunbo Cao

DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (2022.findings-emnlp)

Challenge:	Recent studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation.
Approach:	They propose a novel Contrastive learning method with Prompt-derived Virtual semantic prototypes that constructs virtual semantic prototype to each instance and derives negative prototypes by using the negative form of the prompts.
Outcome:	The proposed method performs on semantic textual similarity, transfer, and clustering tasks compared to baselines.

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Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang, Binghuai Lin, Yunbo Cao

Type-Driven Multi-Turn Corrections for Grammatical Error Correction (2022.findings-acl)

Challenge:	Existing approaches to named entity recognition (NER) are limited to high-resource languages like English and Chinese.
Approach:	They propose a framework to make full use of annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition.
Outcome:	The proposed framework makes full use of both annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER).

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Shaopeng Lai, Qingyu Zhou, Jiali Zeng, Zhongli Li, Chao Li, Yunbo Cao, Jinsong Su

Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion (2023.acl-long)

Challenge:	Existing studies focus on data augmentation to combat exposure bias . but data augmented models lack the ability to recognize the procedure of gradual corrections .
Approach:	They propose a type-driven multi-turn corrections approach that uses multiple training instances to train dominant models.
Outcome:	The proposed model achieves state-of-the-art single-model performance on English GEC benchmarks.

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Shaoxiang Wu, Damai Dai, Ziwei Qin, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui

DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs (2026.acl-long)

Challenge:	Prior denoising methods suppress redundant and noisy information at risk of losing critical information.
Approach:	They propose a denoising bottleneck fusion model for fine-grained video multimodal fusion . they employ a bottleneck mechanism to filter out noise and redundancy with a restrained receptive field .
Outcome:	The proposed model improves on state-of-the-art video multimodal fusion benchmarks.

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Bo Zhang, Jiawei Zhang, Cong Gao, Bingxu Han, Minghao Hu, Jun Zhang, Yunbo Cao, Zhunchen Luo, Wen Yao, Guotong Geng, Zhong Wang

Reasoning

Challenge:	Existing methods for calibration of large reasoning models (LRMs) focus on clean inputs, leaving noise unexplored.
Approach:	They propose a confidence calibration framework for character-level noisy inputs that extracts uncertainty signals from both the empirical answer distribution and the model’s predictive distribution and integrates them via a learned calibrator.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that DisCal outperforms existing calibration methods under noisy inputs, reducing expected calibration error (ECE) by up to 39.21% and improving Area Under the Receiver Operating Characteristic Curve (AUROC) by 31.44%.

Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation (2022.emnlp-main)

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Peiyi Wang, Yifan Song, Tianyu Liu, Binghuai Lin, Yunbo Cao, Sujian Li, Zhifang Sui

Robustness in NLP

Challenge:	Existing studies attribute catastrophic forgetting to the corruption of the learned representations as new relations come . Continual relation extraction models suffer from catastrophic forgetting when learning new relations .
Approach:	They propose to use adversarial class augmentation mechanism to learn more precise representations . they propose to train the model on a sequence of tasks where two new relations are discovered .
Outcome:	The proposed model improves on two popular benchmarks.

HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2022.emnlp-main)

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Zihan Wang, Peiyi Wang, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui, Houfeng Wang

Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems (2022.findings-acl)

Challenge:	Hierarchical text classification (HTC) is a multi-label classification problem with a complex label hierarchy.
Approach:	They propose a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label perspective using a dynamic virtual template and label words that take the form of soft prompts to fuse the label hierarchy knowledge.
Outcome:	The proposed method achieves state-of-the-art performance on 3 popular HTC datasets and is adept at handling imbalance and low resource situations.

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Zhongli Li, Wenxuan Zhang, Chao Yan, Qingyu Zhou, Chao Li, Hongzhi Liu, Yunbo Cao

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	Existing models memorize procedures from context and rely on shallow heuristics to solve MWPs.
Approach:	They propose a contrastive learning approach where the neural network perceives the divergence of patterns.
Outcome:	The proposed method greatly improves performance in monolingual and multilingual settings.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

Hierarchical Curriculum Learning for AMR Parsing (2022.acl-short)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Peiyi Wang, Liang Chen, Tianyu Liu, Damai Dai, Yunbo Cao, Baobao Chang, Zhifang Sui

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

Challenge:	Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, but there is a gap between their flat training objective and the hierarchic structure, which limits the model generalization.
Approach:	They propose a Hierarchical Curriculum Learning framework with Structure-level (SC) and Instance-level curricula (IC) that aims to translate sentences to semantic representation with a hierarchical structure.
Outcome:	Experiments on AMR2.0, AMR3.0, structure-complex and out-of-distribution situations confirm the effectiveness of the proposed framework.

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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 .

Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)

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Heng-Da Xu, Zhongli Li, Qingyu Zhou, Chao Li, Zizhen Wang, Yunbo Cao, Heyan Huang, Xian-Ling Mao

DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection (2022.findings-emnlp)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese.
Approach:	They propose a Chinese spell checker that leverages multimodal Chinese characters' information to predict the correct output.
Outcome:	The proposed model outperforms strong baselines on the SIGHAN benchmarks by a large margin.

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Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, Zhifang Sui

Natural Language Interfaces

Challenge:	DialogUSR is a plug-in and domain-agnostic module that empowers multi-intent detection for chatbots . a single user query triggers inquiries on highspeed train ticket price and weather of destination.
Approach:	They propose a dialog utterance splitting and reformulation task that splits multi-intent user query into multiple single-intention sub-queries and recovers all coreferred and omitted information in the sub-questions.
Outcome:	The proposed model can be used to split multi-intent user queries into multiple sub-queries . it can be trained in two stages and perform in-depth analyses on the proposed models .

SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking (2026.acl-long)

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Yingjie Xue, Xingyou Xia, Jun Zhang, Yunbo Cao, Dengpan Ye, Guotong Geng, Fei Li

Responsible & Trustworthy NLP

Challenge:	Existing methods for jailbreak ignore the semantic differences between categories of harmful questions, leading to inconsistent success rates and reduced overall attack effectiveness.
Approach:	They propose a category-aware jailbreak framework that incorporates the semantic category of harmful questions into prompt generation.
Outcome:	The proposed framework improves attack success rates and category alignment and achieves better cross-category robustness compared to the state-of-the-art (SOTA) baselines.

Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation (2020.findings-emnlp)

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Chujie Zheng, Yunbo Cao, Daxin Jiang, Minlie Huang

Dialogue Systems & Conversational Agents

Challenge:	Existing knowledge selection models are limited by the context, but the difference between selected knowledge at different turns is often overlooked.
Approach:	They propose a difference-aware knowledge selection method that computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns.
Outcome:	The proposed method outperforms the state-of-the-art methods in a knowledge-grounded dialog.

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

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

Programming Languages in NLP

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.

When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026.findings-acl)

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Mingxu Tao, Jiawei Hu, Xian Zhou, Wenpeng Hu, Jiajun Cheng, Yunbo Cao, Zhunchen Luo, Guotong Geng

Paraphrasing

Challenge:	Existing dense retrieval methods have achieved notable progress, but their effectiveness in legal case retrieval remains limited.
Approach:	They propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training.
Outcome:	The proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, especially when powered by a high-capacity core LLM.

AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications (2022.coling-1)

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Yusen Zhang, Zhongli Li, Qingyu Zhou, Ziyi Liu, Chao Li, Mina Ma, Yunbo Cao, Hongzhi Liu

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)

Challenge:	Existing methods to correct handwritten assignments are to use OCR to recognize characters and compare them to answers.
Approach:	They propose a multimodal approach to correct handwritten Chinese characters by combining the visual information of students' handwriting with the encoded representations of answers.
Outcome:	The proposed model outperforms OCR-based methods by a large margin.

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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen

Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking (2022.findings-emnlp)

Challenge:	Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life.
Approach:	They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors.
Outcome:	The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development.

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Yinghui Li, Shirong Ma, Qingyu Zhou, Zhongli Li, Li Yangning, Shulin Huang, Ruiyang Liu, Chao Li, Yunbo Cao, Haitao Zheng

An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling (2022.naacl-main)

Challenge:	Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors.
Approach:	They propose a framework which renders Chinese Spell Checking model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.
Outcome:	The proposed framework renders the CSC model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning.

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Peiyi Wang, Runxin Xu, Tianyu Liu, Qingyu Zhou, Yunbo Cao, Baobao Chang, Zhifang Sui

Improving BERT with Syntax-aware Local Attention (2021.findings-acl)

Challenge:	Existing approaches to tagging tasks are limited to predefined classes and require large-scale annotated data.
Approach:	They propose an Enhanced Span-based Decomposition method for Few-Shot Sequence Labeling to generalize on emerging, resource-scare domains.
Outcome:	The proposed method achieves state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is more robust in noisy and nested tagging scenarios.

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Zhongli Li, Qingyu Zhou, Chao Li, Ke Xu, Yunbo Cao

Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning (2021.findings-acl)

Challenge:	Recent studies show that attention-based models benefit from more focused attention over local regions.
Approach:	They propose a syntax-aware local attention which restrains attention over syntactically relevant words.
Outcome:	The proposed model performs better on all benchmark datasets, including sentence classification and sequence labeling tasks.

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Ximing Zhang, Qian-Wen Zhang, Zhao Yan, Ruifang Liu, Yunbo Cao

Dialogue Response Selection with Hierarchical Curriculum Learning (2021.acl-long)

Challenge:	Existing approaches to multi-task learning fail to capture label correlations . Existing methods suffer from label order dependency, label combination over-fitting and error propagation problems.
Approach:	They propose a novel approach with multi-task learning to enhance label correlation feedback.
Outcome:	The proposed method outperforms baselines on AAPD and RCV1-V2 datasets.

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Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang

Dialogue Systems & Conversational Agents

Challenge:	Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.
Approach:	They propose a hierarchical curriculum learning framework that trains matching models in an “easy-to-difficult” scheme.
Outcome:	The proposed framework significantly improves the model performance across evaluation metrics on three benchmark datasets with three state-of-the-art matching models.

Diversity and Consistency: Exploring Visual Question-Answer Pair Generation (2021.findings-emnlp)

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Sen Yang, Qingyu Zhou, Dawei Feng, Yang Liu, Chao Li, Yunbo Cao, Dongsheng Li

A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021.findings-emnlp)

Challenge:	Existing tasks to generate question-answer pairs from visual images are under-explored.
Approach:	They propose a task that targets question-answer pair generation from visual images.
Outcome:	The proposed model can generate diverse or consistent QAPs on two benchmarks.

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Deyu Zhou, Yanzheng Xiang, Linhai Zhang, Chenchen Ye, Qian-Wen Zhang, Yunbo Cao

Natural Language Interfaces

Challenge:	Existing methods for relation detection only detect one path to obtain the answer without considering other correct paths.
Approach:	They propose a divide-and-conquer approach for multi-label multi-hop relation detection . they propose 'path sampling mechanism' to generate diverse relation paths .
Outcome:	The proposed approach outperforms other competitive approaches on the FreebaseQA benchmark dataset.

DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade (2024.naacl-long)

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Zefan Cai, Xin Zheng, Tianyu Liu, Haoran Meng, Jiaqi Han, Gang Yuan, Binghuai Lin, Baobao Chang, Yunbo Cao

Dialogue Systems & Conversational Agents

Challenge:	Existing models for natural language understanding are based on a well-defined intent 1 ontology.
Approach:	They propose to retrain the natural language understanding model as new data from real users are merged into existing data.
Outcome:	The proposed model shows that the semantically entangled intents can be recognized with an automatic workflow.

G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation (2023.findings-acl)

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Yanzheng Xiang, Qian-Wen Zhang, Xu Zhang, Zejie Liu, Yunbo Cao, Deyu Zhou

The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking (2022.findings-acl)

Challenge:	Existing approaches to complex and cross-domain Text-to-SQL generation lack domain knowledge . domain knowledge is not incorporated to enhance their ability to generalise to unseen databases.
Approach:	They propose a framework called G3R for complex and cross-domain Text-to-SQL generation . they propose re-ranking SQL queries based on domain knowledge and a graph-guided SQL generator .
Outcome:	The proposed framework achieves state-of-the-art results on the Spider and Spider-DK benchmarks.

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Yinghui Li, Qingyu Zhou, Yangning Li, Zhongli Li, Ruiyang Liu, Rongyi Sun, Zizhen Wang, Chao Li, Yunbo Cao, Hai-Tao Zheng