Papers by Junyu Lu

Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models (2024.emnlp-main)

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Rui Li, Qi Liu, Liyang He, Zheng Zhang, Hao Zhang, Shengyu Ye, Junyu Lu, Zhenya Huang

Challenge:	Existing methods for code retrieval struggle to balance scalability and annotation quality.
Approach:	They propose a method that integrates functions called within the repository and information on third-party APIs to enhance the annotation context.
Outcome:	The proposed method improves the annotation context by incorporating functions called within the repository and information on third-party API functionalities.

PclGPT: A Large Language Model for Patronizing and Condescending Language Detection (2024.findings-emnlp)

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Hongbo Wang, LiMingDa LiMingDa, Junyu Lu, Hebin Xia, Liang Yang, Bo Xu, Ruizhu Liu, Hongfei Lin

Language Models

Challenge:	Patronizing and condescending language is an essential branch of toxic language . pre-trained language models perform poorly in detecting PCL due to its implicit toxicity traits .
Approach:	They propose a novel LLM benchmark for patronizing and condescending language . they use a dataset to analyze the toxicity of patronizing condescending languages .
Outcome:	The proposed model can detect patronizing and condescending language (PCL) the model can be used to analyze the toxicity of the language and to improve the detection.

Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement (2025.findings-acl)

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Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, Hongfei Lin

Responsible & Trustworthy NLP

Challenge:	Existing studies focus on evaluating large language models' ability to handle disagreement cases.
Approach:	They evaluate the performance of large language models in detecting offensive language at varying levels of agreement.
Outcome:	The proposed model improves detection accuracy and model alignment with human judgment by using disagreement samples in training.

Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)

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Junyu Lu, Chenbin Zhang, Zeying Xie, Guang Ling, Tom Chao Zhou, Zenglin Xu

Natural Language Interfaces

Challenge:	Existing models for response selection do not perform well when there are many candidate responses.
Approach:	They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response .
Outcome:	The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks.

STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection (2025.findings-acl)

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Zewen Bai, Liang Yang, Shengdi Yin, Junyu Lu, Jingjie Zeng, Haohao Zhu, Yuanyuan Sun, Hongfei Lin

Ethical NLP

Challenge:	Existing studies on Chinese hate speech detection lack span-level fine-grained annotations.
Approach:	They construct a Span-level target-aware Toxicity Extraction dataset and evaluate existing models for Chinese hateful slang.
Outcome:	The proposed dataset is the first span-level Chinese hate speech dataset and evaluates the ability of existing models to understand hate semantics.

SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)

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Wazir Ali, Junyu Lu, Zenglin Xu

Information Extraction & Text Mining

Challenge:	Named entity recognition is an essential lower-level task in natural language processing (NLP).
Approach:	They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines.
Outcome:	The proposed dataset is likely to be a significant resource for statistical Sindhi language processing.

CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval (2025.findings-acl)

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Heng Yu, Junfeng Kang, Rui Li, Qi Liu, Liyang He, Zhenya Huang, Shuanghong Shen, Junyu Lu

Information Retrieval

Challenge:	Recent techniques such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieleval (GDR) leverage LLMs to enhance retrieval performance but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLM.
Approach:	They propose a Context-Aware Generation-Augmented Retrieval approach which integrates corpus information into their generation process.
Outcome:	Experimental results show that CA-GAR outperforms existing methods on seven tasks and four non-English languages.

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

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

Speech & Audio in NLP

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

Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition (2025.naacl-long)

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Haohao Zhu, Xiaokun Zhang, Zeyuan Zeng, Junyu Lu, Zewen Bai, Liang Yang, Hongfei Lin

Commonsense Reasoning

Challenge:	Current methods for humor recognition focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor.
Approach:	They propose a commonality and individuality incorporated network for humor recognition that integrates multifaceted humor commonalities with speaker individuality.
Outcome:	The proposed model integrates multifaceted humor commonalities with speaker individuality to deepen the understanding of humor expressions.

Flat Multi-modal Interaction Transformer for Named Entity Recognition (2022.coling-1)

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Junyu Lu, Dixiang Zhang, Jiaxing Zhang, Pingjian Zhang

Multimodality

Challenge:	Named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images.
Approach:	They propose to use sentences and general domain words to obtain visual cues to transform the fine-grained semantic representation of vision and text into a unified lattice structure and leverage entity boundary detection as an auxiliary task to alleviate visual bias.
Outcome:	The proposed method achieves state-of-the-art on two benchmark datasets.

Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect (2024.lrec-main)

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Junyu Lu, Bo Xu, Xiaokun Zhang, Kaiyuan Liu, Dongyu Zhang, Liang Yang, Hongfei Lin

Responsible & Trustworthy NLP

Challenge:	Existing methods to mitigate lexical bias in toxic language detection (TLD) do not exploit the “useful” and “misleading” impact of the bias.
Approach:	They propose a counterfactual Causal Debiasing Framework to mitigate lexical bias in toxic language detection (TLD) it preserves the “useful impact” of lexical bias and eliminates the "misleading impact" they propose to use the same framework to analyze the causal effect of a sentence and bias tokens .
Outcome:	The proposed framework preserves the “useful impact” of lexical bias and eliminates the ‘misleading impact’ Empirical evaluations show that the proposed model outperforms current debiased models for out-of-distribution data.

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)

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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu

Responsible & Trustworthy NLP

Challenge:	Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets.
Approach:	They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring.
Outcome:	The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring .

Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks (2023.acl-long)

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Junyu Lu, Bo Xu, Xiaokun Zhang, Changrong Min, Liang Yang, Hongfei Lin

Challenge:	Existing datasets suffer from a lack of fine-grained annotations, such as the toxic type and expressions with indirect toxicity.
Approach:	They propose a benchmark model to detect toxic language by incorporating lexical features into a Chinese dataset to facilitate fine-grained annotations.
Outcome:	The proposed model is based on insulting vocabulary containing implicit profanity and is able to detect toxic language with lexical features.