Papers by Yufeng Wang
Continual Learning with Semi-supervised Contrastive Distillation for Incremental Neural Machine Translation (2024.acl-long)
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| Challenge: | Multi-domain learning is a good solution for solving domain tasks but it requires retraining when adding a new domain. |
| Approach: | They propose to exploit unlabeled data from the same distributions of the older domains to avoid catastrophic forgetting. |
| Outcome: | The proposed framework exploits unlabeled data from the same distributions of the older domains to avoid catastrophic forgetting. |
CTSM: Combining Trait and State Emotions for Empathetic Response Model (2024.lrec-main)
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| Challenge: | Empathetic response generation attempts to empower dialogue systems to perceive speakers’ emotions and generate empathetic responses accordingly. |
| Approach: | They propose to combine trait and state emotions for Empathetic Response Model to enable dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates more empathetic responses. |
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing knowledge distillation techniques for neural machine translation lack special treatment on the top-1 information, which is limiting the potential of KD. |
| Approach: | They propose a method to distill knowledge from top-1 predictions of teachers and a technique to infuse more additional knowledge by distilling on the data without ground-truth targets. |
| Outcome: | The proposed method outperforms the vanilla word-level KD and outperfies the existing methods on three different students with different capacity gaps. |
D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization (2023.findings-emnlp)
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| Challenge: | Existing studies focus on improving MMS models by filtering summary-unrelated visual features with implicit learning or explicitly complex training objectives. |
| Approach: | They propose a multimodal multimodal summarization task that aims to generate summaries in any language with document inputs in any languages and the corresponding image sequence. |
| Outcome: | The proposed task can generate summaries in any language with document inputs in any languages and the corresponding image sequence. |
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)
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Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao
| Challenge: | Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue. |
| Approach: | They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions. |
| Outcome: | The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses. |
Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement (2025.naacl-long)
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| Challenge: | Existing methods for long-form story generation rely on rigid outlines or lack macro-level planning, making it difficult to achieve contextual consistency and coherent plot development. |
| Approach: | They propose a Dynamic Hierarchical Outlining with Memory-Enhancement long-form story generation method to generate long-formed story with coherent content and plot. |
| Outcome: | The proposed method significantly improves the fluency, coherence, and overall quality of generated long stories compared to state-of-the-art methods. |
MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints (2026.findings-acl)
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Kangyang Luo, Shuzheng Si, Yuzhuo Bai, Cheng Gao, Zhitong Wang, Cheng Huang, Yingli Shen, Yufeng Han, Wenhao Li, Cunliang Kong, Maosong Sun
| Challenge: | Existing supervised neural methods are underexplored for coreference resolution, especially in incremental clustering. |
| Approach: | They propose a dual-threshold incremental clustering approach based on a lightweight Transformer. |
| Outcome: | Experiments on common benchmarks show that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints. |
Outdated Issue Aware Decoding for Factual Knowledge Editing (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods retain outdated responses for reasoning questions . naively retraining LLMs can be computationally intensive and can lead to catastrophic forgetting . |
| Approach: | They propose a simple yet effective decoding strategy to enhance edited models on reasoning questions. |
| Outcome: | The proposed method outDates ISsue aware deCOding (DISCO) to improve models on reasoning questions. |
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)
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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang
| Challenge: | Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections. |
| Approach: | They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. |
| Outcome: | The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. |
ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement (2026.acl-long)
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Kangyang Luo, Yuzhuo Bai, Shuzheng Si, Cheng Gao, Zhitong Wang, Yingli Shen, Wenhao Li, Zhu Liu, Yufeng Han, Jiayi Wu, Cunliang Kong, Maosong Sun
| Challenge: | Existing supervised neural methods for coreference resolution are underexplored . current methods rely on small language models, but their potential is underexploited . |
| Approach: | They propose a framework that integrates an enhanced supervised model with LLM-based reasoning. |
| Outcome: | The proposed method surpasses existing state-of-the-art methods in coreference resolution. |
SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering? (2026.findings-acl)
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Yuxuan Sun, Yuze Zhao, Yufeng Wang, Yao Du, Zhiyuan Ma, Jinbo Wang, Mengdi Zhang, Kai Zhang, Zhenya Huang
| Challenge: | Evaluating software engineering capabilities is a core component of large language models (LLMs). |
| Approach: | They propose a benchmark to evaluate LLM-generated test suites that introduces mutated solutions that attempt to "fool" them. |
| Outcome: | The proposed test suites are based on 2,636 mutated variants derived from 800 original instances and include a multilingual subset spanning nine programming languages. |
Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)
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| Challenge: | Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space. |
| Approach: | They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples. |
| Outcome: | The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages. |
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization (2023.acl-long)
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| Challenge: | Existing studies on multimodal abstractive summarization focus on how to use extracted visual features to produce a concise summary given the multimodal data. |
| Approach: | They propose to improve the visual quality of the multimodal abstractive summarization model by capturing summary-oriented visual features. |
| Outcome: | The proposed approach achieves state-of-the-art under 44 languages and is highly effective on high-resource English datasets. |
C3D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding (2026.findings-acl)
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| Challenge: | Large language models are prone to distraction by contextual information during reasoning tasks. |
| Approach: | They propose a decoding method that uses predicted logits to estimate the model's confidence. |
| Outcome: | The proposed method reveals how the model dynamically activates and adjusts its consideration of each premise as reasoning progresses. |
An Iterative Associative Memory Model for Empathetic Response Generation (2024.acl-long)
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| Challenge: | Existing methods for empathetic response generation ignore the associated words between dialogue utterances. |
| Approach: | They propose an iterative associative memory model to capture associated words between dialogue utterances and situations, dialogue history, and a memory module for storing associated words. |
| Outcome: | The proposed model captures key words between dialogue utterances and situations, dialogue history, and a memory module, thereby accurately and nuancedly comprehending the utterables. |
Adversarially Improving NMT Robustness to ASR Errors with Confusion Sets (2022.aacl-short)
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| Challenge: | Existing methods for robustness against homophone errors are limited to homophones . substitution errors are the most common errors in NMT models . |
| Approach: | They propose an adversarial example generation method based on confusion sets that contain words easily confusable with a target word by ASR to conduct adversarially training for NMT models. |
| Outcome: | The proposed method improves on the clean test set and can be used in real-world scenarios. |
Exploration-Exploitation Reshaping towards Efficient Reasoning for Large Language Models (2026.findings-acl)
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Yufeng Shi, Weilin Luo, Yuxiang Zhang, Zongmeng Zhang, Haoyang Liu, Yubing Wang, Bin Wang, Wengang Zhou, Houqiang Li
| Challenge: | Large Reasoning Models (LRMs) are constrained by the overthinking issue. |
| Approach: | They propose a policy optimization framework that reshapes the exploration and exploitation through two core components: self-imitation and self-guidance exploration. |
| Outcome: | The proposed model achieves superior reasoning efficiency without compromising overall accuracy. |
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
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| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark datasets. |
SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning (2026.findings-acl)
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Jichao Wang, Liuyang Bian, Yufeng Zhou, Han Xiao, Yue Pan, Guozhi Wang, Hao Wang, Zhaoxiong Wang, Yafei Wen, Xiaoxin Chen, Shuai Ren, Lingfang Zeng
| Challenge: | Existing approaches to training GUI agents on dynamic tasks are based on SFT or Behavior Cloning. |
| Approach: | They propose a framework that integrates global trajectory insights directly into offline learning . they reconstruct diverse rollout candidates from static data and detect first failure point . |
| Outcome: | The proposed framework improves long-horizon task completion rates and robustness compared to baselines. |
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)
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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
| Challenge: | Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes. |
| Approach: | They propose a WordNet-encoded model to settle polysemy of homographic puns and a word weighted model for recognizing them. |
| Outcome: | The proposed model can distinguish between homographic pun and non-homographic pun texts. |