Papers by Jian Yin

Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition (2025.coling-main)

Challenge:	Adapting a model to a handful of personalized data is challenging, authors say . standard fine-tuning requires hundreds of millions of parameters for each user .
Approach:	They propose a lightweight method that clamps millions of parameters of a Transformer model and optimizes a tiny user-specific vector.
Outcome:	The proposed method improves accuracy on Yelp and IMDB datasets and reduces the number of parameters added for each user.

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Jianxing Yu, Shiqi Wang, Hanjiang Lai, Wenqing Chen, Yanghui Rao, Qinliang Su, Jian Yin

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

Challenge:	Existing work mainly learns to map text into questions, lacking a mechanism to control results with desired complexity.
Approach:	They propose a novel controllable framework to generate QGs with desired complexity using contextual and commonsense clues from text.
Outcome:	The proposed framework can generate complex questions with desired complexity levels.

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

Multilinguality

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.

Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder (2020.acl-main)

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Daya Guo, Duyu Tang, Nan Duan, Jian Yin, Daxin Jiang, Ming Zhou

UnCo: Uncertainty-Driven Collaborative Framework of Large and Small Models for Grounded Multimodal NER (2025.emnlp-main)

Challenge:	Existing approaches for inferential text generation ignore context that is not explicitly provided . Existing models ignore background knowledge that provides crucial evidence to generate inferences .
Approach:	They propose an approach that automatically finds evidence for an event from a large text corpus and leverages it to guide the generation of inferential texts.
Outcome:	The proposed model generates inferential texts from a large text corpus and uses evidence to guide it.

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Jielong Tang, Yang Yang, Jianxing Yu, Zhen-Xing Wang, Haoyuan Liang, Liang Yao, Jian Yin

Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning (2025.coling-main)

Challenge:	Existing methods to identify unseen multimodal entities struggle with limited knowledge and generalization.
Approach:	They propose a framework that leverages the strengths of small fine-tuned models and MLLMs to generate unambiguous predictions.
Outcome:	Extensive experiments show that the proposed framework retains the in-domain knowledge of small models while utilizing the capabilities of MLLMs to handle unseen entities.

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Ziqi Qiu, Jianxing Yu, Yufeng Zhang, Hanjiang Lai, Yanghui Rao, Qinliang Su, Jian Yin

Sentiment Analysis

Challenge:	Existing methods for sarcasm detection lack commonsense inferential ability when faced with complex situations.
Approach:	They propose a commonsense reasoning framework for sarcasm detection based on commonsensense augmentation to supplement commonsence knowledge and infer the incongruity.
Outcome:	The proposed framework is able to detect sarcasm in five datasets and is robust to complex scenarios.

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

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

Multilinguality

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.

Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)

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Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou

Text Generation

Challenge:	Using question generation, we learn a semantic parser with 30% of the supervised training data.
Approach:	They propose to use question generation to learn a semantic parser with less supervised training data.
Outcome:	The proposed method improves the state-of-the-art model with less training data.

ProQA: Structural Prompt-based Pre-training for Unified Question Answering (2022.naacl-main)

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Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan

Eliciting Implicit Acoustic Styles from Open-domain Instructions to Facilitate Fine-grained Controllable Generation of Speech (2025.emnlp-main)

Challenge:	Existing QA research on question answering is focused on specific question types, knowledge domains, or reasoning skills.
Approach:	They propose a unified QA paradigm that solves various tasks through a single model.
Outcome:	The proposed model improves QA-centric ability on 11 QA benchmarks.

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Jianxing Yu, Gou Zihao, Chen Li, Zhisheng Wang, Peiji Yang, Wenqing Chen, Jian Yin

Domain Adaptation for Subjective Induction Questions Answering on Products by Adversarial Disentangled Learning (2024.acl-long)

Challenge:	Current work relies on pre-defined rules or templates to control the style of speech.
Approach:	They propose to use open-domain instructions to generate speech with the acoustic style that meets users’ needs based on their instructions.
Outcome:	The proposed model can be used to generate speech with the acoustic style that meets users’ needs based on open-domain instructions.

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Yufeng Zhang, Jianxing Yu, Yanghui Rao, Libin Zheng, Qinliang Su, Huaijie Zhu, Jian Yin

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

Challenge:	Existing methods to answer subjective questions about products are often imbalanced across product domains.
Approach:	They propose a domain-adaptive model that integrates multiple viewpoints into a good answer by integrating these heterogeneous and inconsistent viewpoints.
Outcome:	The proposed model integrates multiple viewpoints into a single answer span and is able to integrate them into the answer.

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

Low-Resource NLP

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

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

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

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)

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.

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Zhiping Luo, Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

FNSCC: Fuzzy Neighborhood-Aware Self-Supervised Contrastive Clustering for Short Text (2025.findings-emnlp)

Challenge:	Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples .
Approach:	They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE.
Outcome:	The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR .

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Zijian Zheng, Yonghe Lu, Jian Yin

Finding RELIEF: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineering (2026.findings-acl)

Challenge:	Short texts pose significant challenges for clustering due to semantic sparsity, limited context and fuzzy category boundaries.
Approach:	proposed framework incorporates neighborhood information at instance and cluster levels . a cluster-level framework introduces fuzzy neighborhood-aware weighting .
Outcome:	The proposed framework outperforms state-of-the-art models on short texts . it excludes neighbors from negative sample set to enhance inter-cluster separability .

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Chak Tou Leong, Dingwei Chen, Heming Xia, Qingyu Yin, Sunbowen Lee, Jian Wang, Wenjie Li

Using Intermediate Representations to Solve Math Word Problems (P18-1)

Challenge:	Existing methods for shaping large reasoning models rely on reinforcement learning or fine-tuning with gold-standard reasoning traces. Existing techniques for behavior shaping rely only on additional reward modeling.
Approach:	They propose a framework that aligns a model's self-concept with a target belief blueprint and internalizes desired traits by fine-tuning on synthesized, self-reflective QA pairs that affirm the target belief.
Outcome:	The proposed framework outperforms behavior-supervised and preference-based models while requiring significantly lower training costs.

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Danqing Huang, Jin-Ge Yao, Chin-Yew Lin, Qingyu Zhou, Jian Yin

Numerical Reasoning

Challenge:	Existing approaches to solving math word problems do not include higher-order operations that cannot be explicitly represented in equations.
Approach:	They propose an iterative labeling framework that generates intermediate forms and executes them to obtain the final answers.
Outcome:	The proposed model outperforms existing models in solving math word problems.

UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022.acl-long)

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Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, Jian Yin

Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)

Challenge:	Pre-trained models for programming languages have demonstrated great success on code intelligence . however, such pre-tried models are sub-optimal for auto-regressive tasks .
Approach:	They propose a unified cross-modal pre-trained model for programming language that leverages cross-module contents like AST and code comment to enhance code representation.
Outcome:	The proposed model achieves state-of-the-art on most code-related tasks and compares with existing models on zero-shot code-to-code search.

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Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Conflicts Make Large Reasoning Models Vulnerable to Attacks (2026.findings-acl)

Challenge:	Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences.
Approach:	They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure .
Outcome:	The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset.

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Honghao Liu, Chengjin Xu, Xuhui Jiang, Cehao Yang, Shengming Yin, Zhengwu Ma, Lionel Ni, Jian Guo

CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models (2025.coling-main)

Challenge:	Large Reasoning Models have demonstrated outstanding capabilities in solving complex reasoning tasks by incorporating step-by-step chain-of-thought (CoT) reasoning.
Approach:	They evaluate three large reasoning models that perform explicit and coherent reasoning under conflicting objectives and use them to evaluate their performance.
Outcome:	The proposed models perform explicit and coherent reasoning before producing their outputs, improving problem-solving and multi-step decision making.

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Zhongzhi Li, Ming-Liang Zhang, Pei-Jie Wang, Jian Xu, Rui-Song Zhang, Yin Fei, Zhi-Long Ji, Jin-Feng Bai, Zhen-Ru Pan, Jiaxin Zhang, Cheng-Lin Liu

Numerical Reasoning

Challenge:	Large language models excel in various language tasks, while large multimodal models effectively handle visual-language problems.
Approach:	They propose to use a multimodal multimodal model evaluation benchmark to evaluate model performance in Chinese K12 classrooms.
Outcome:	The proposed model evaluation tool is integrated with the CMMaTH dataset.

Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference (2023.findings-acl)

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Jianxing Yu, Shiqi Wang, Libin Zheng, Qinliang Su, Wei Liu, Baoquan Zhao, Jian Yin

Neural Math Word Problem Solver with Reinforcement Learning (C18-1)

Challenge:	Existing methods for commonsense question generation produce shallow questions that can be answered by simple word matching.
Approach:	They propose a task of commonsense question generation that aims to yield deep-level questions from the text.
Outcome:	The proposed model can yield deep-level and to-the-point questions from the text.

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Danqing Huang, Jing Liu, Chin-Yew Lin, Jian Yin

SEEK: Segmented Embedding of Knowledge Graphs (2020.acl-main)

Challenge:	Existing models for solving math word problems rely on predefined rules or feature engineering.
Approach:	They propose to incorporate copy and alignment mechanism into the sequence-to-sequence model to address two shortcomings . they use model output as a feature and incorporate it into the feature-based model to explore the effectiveness .
Outcome:	The proposed model outperforms the state-of-the-art models on the problem solving task.

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Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu

DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023.acl-long)

Challenge:	Existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them far from satisfactory.
Approach:	They propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity.
Outcome:	The proposed framework can achieve highly competitive relational expressiveness without increasing model complexity.

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Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, Xiaodan Liang

From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base (2023.emnlp-main)

Challenge:	Recent advances in neural theorem-proving resort to large language models and tree searches.
Approach:	They propose a Dynamic-Tree Driven Theorem Solver to accommodate general theoremes by guiding the search procedure with state confidence and proof-level values.
Outcome:	The proposed method outperforms state-of-the-art methods on two popular theorem-proving datasets with a 6.65% improvement on average in terms of success rate.

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Wangzhen Guo, Linyin Luo, Hanjiang Lai, Jian Yin

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

Challenge:	Existing methods for parsing knowledge-base questions into executable logical forms have not been successful on complex KBQA.
Approach:	They propose a new semantic parser called KoPL to model the reasoning processes . they propose 'parse-execute-refine' paradigm to unlock reasoning ability .
Outcome:	The proposed parser performs better than the state-of-the-art on complex KBQA . the proposed parsed-execute-refine paradigm can model complex reasoning steps .

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

Multilinguality

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.

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

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

Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (P19-1)

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 .

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Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin

Analytical Reasoning of Text (2022.findings-naacl)

Challenge:	a context-aware retrieval model and a meta-learning paradigm are used for context-dependent semantic parsing .
Approach:	They propose a retrieval model and a meta-learner to incorporate retrieved datapoints as context-dependent semantic parsing evidence.
Outcome:	The proposed approach performs better than retrieve-and-edit baselines on CONCODE and CSQA datasets.

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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)

Challenge:	Existing models with implicit reasoning ability struggle to solve analytical reasoning of text.
Approach:	They propose an approach to analyze text and use it to perform reasoning over it.
Outcome:	The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset.

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Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Structured Data in NLP

Challenge:	Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents.
Approach:	They propose a graph-based model that captures factual structures of documents for deepfake detection.
Outcome:	The proposed model improves strong base models built with RoBERTa on two public deepfake datasets.

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

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Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin

Ambiguity Awareness Optimization: Towards Semantic Disambiguation for Direct Preference Optimization (2025.emnlp-main)

Challenge:	Existing methods for fact checking textual statements are not yet available.
Approach:	They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome:	The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .

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Jian Li, Shenglin Yin, Yujia Zhang, Alan Zhao, Xi Chen, Xiaohui Zhou, Pengfei Xu

Why Safeguarded Ships Run Aground? Aligned Large Language Models’ Safety Mechanisms Tend to Be Anchored in The Template Region (2025.acl-long)

Challenge:	Direct Preference Optimization (DPO) is a widely used reinforcement learning from human feedback (RLHF) method across various domains.
Approach:	They propose an approach that automatically re-weights ambiguous content to reduce ambiguities by calculating semantic similarity from preference pairs.
Outcome:	The proposed approach outperforms state-of-the-art approaches in performance across multiple model scales and widely adopted benchmark datasets.

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Chak Tou Leong, Qingyu Yin, Jian Wang, Wenjie Li

Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)

Challenge:	Infilling a fixed template between the input instruction and initial model output is a common practice for existing LLMs, but it is vulnerable to inference-time jailbreak attacks.
Approach:	They propose to fill a fixed template between the input instruction and initial model output and to detach safety mechanisms from the template region to mitigate the risk of inference-time jailbreak attacks.
Outcome:	The proposed method is widespread across aligned LLMs and shows that it mitigates inference-time jailbreak vulnerabilities.

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Jianxing Yu, Zhengjun Zha, Jian Yin

Natural Language Interfaces

Challenge:	Experimental results on 3 popular datasets demonstrate the effectiveness of our approach.
Approach:	They propose a network to solve the inference problem by decomposing text into a series of attention-based reasoning steps.
Outcome:	The proposed network can be used to understand the meanings of given text to answer questions.

Answering Cross-Dimensional Geometric Visual Questions by Multi-constraint Spatial Reasoning (2026.findings-acl)

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Dongling Li, Qi Chen, Jianxing Yu, Hanjiang Lai, Yanghui Rao, Wenqing Chen, Jian Yin

Visual Data in NLP

Challenge:	Existing methods for solving complex visual questions are limited in their ability to represent in a cross-dimensional space.
Approach:	They propose a method that can answer complex visual questions using cross-dimensional reasoning.
Outcome:	The proposed method can answer complex visual questions in 2D to 3D space with great application value.

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)

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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai

Answering Complex Geographic Questions by Adaptive Reasoning with Visual Context and External Commonsense Knowledge (2025.acl-long)

Challenge:	Existing studies on biases within specific domains, such as finance, remain limited.
Approach:	They propose a framework to detect, detect, analyze and mitigate financial biases in large language models.
Outcome:	The proposed framework reduces bias by 68% for the most biased model, according to key metrics.

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Fan Li, Jianxing Yu, Jielong Tang, Wenqing Chen, Hanjiang Lai, Yanghui Rao, Jian Yin

Improving Speech Translation by Fusing Speech and Text (2023.findings-emnlp)

Challenge:	a new task of answering geographic reasoning questions based on the given image is proposed . the task requires identifying the objects in the image and understanding the background context .
Approach:	They propose a task of answering geographic reasoning questions based on the given image . they analyze the image and describe its fine-grained content by text and keywords .
Outcome:	The proposed method can be used to answer geographic reasoning questions based on an image . it can be applied to a large-scale dataset with 41,329 samples .

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Wenbiao Yin, Zhicheng Liu, Chengqi Zhao, Tao Wang, Jian Tong, Rong Ye