Papers by Yufeng Chen

Continual Learning with Semi-supervised Contrastive Distillation for Incremental Neural Machine Translation (2024.acl-long)

Challenge:	Existing IE tools lack multi-task support and automatic updates for KG and EKG construction.
Approach:	They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine.
Outcome:	The proposed tool improves annotation quality, efficiency, and stability simultaneously.

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Yunlong Liang, Fandong Meng, Jiaan Wang, Jinan Xu, Yufeng Chen, Jie Zhou

CTSM: Combining Trait and State Emotions for Empathetic Response Model (2024.lrec-main)

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.

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Yufeng Wang, Chao Chen, Zhou Yang, Shuhui Wang, Xiangwen Liao

Dialogue Systems & Conversational Agents

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 Making the Most of Dialogue Characteristics for Neural Chat Translation (2021.emnlp-main)

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Yunlong Liang, Chulun Zhou, Fandong Meng, Jinan Xu, Yufeng Chen, Jinsong Su, Jie Zhou

Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation (2023.acl-long)

Challenge:	Neural Chat Translation (NCT) models that use dialogue characteristics of chat are often incoherent and speakerirrelevant.
Approach:	They propose to introduce the modeling of dialogue characteristics into the NCT model by capturing the inherent dialogue characteristics.
Outcome:	The proposed model can translate conversational text between speakers of different languages.

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Songming Zhang, Yunlong Liang, Shuaibo Wang, Yufeng Chen, Wenjuan Han, Jian Liu, Jinan Xu

Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2022.acl-long)

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.

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Songming Zhang, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jian Liu, Jie Zhou

A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis (D19-1)

Challenge:	Existing approaches to improve neural machine translation use token-level adaptive training . however, standard models make predictions on condition of previous contexts .
Approach:	They propose a target-context-aware metric which can be supplemented by statistical metrics . they propose an adaptive training approach based on token- and sentence-level CBMI .
Outcome:	The proposed model outperforms the Transformer baseline and other similar approaches on English-German and Chinese-English tasks.

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Yunlong Liang, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie Zhou

Sentiment Analysis

Challenge:	Existing models use aspect-independent encoders for sentence representation generation.
Approach:	They propose an aspect-guided deep transition model which guides the sentence encoding from scratch with a specially-designed deep transition architecture.
Outcome:	The proposed model outperforms existing models on multiple datasets on aspect-category sentiment analysis and aspectterm sentiment analysis without additional features.

A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)

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Yunlong Liang, Fandong Meng, Chulun Zhou, Jinan Xu, Yufeng Chen, Jinsong Su, Jie Zhou

D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization (2023.findings-emnlp)

Challenge:	Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective.
Approach:	They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder .
Outcome:	The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting.

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Yunlong Liang, Fandong Meng, Jiaan Wang, Jinan Xu, Yufeng Chen, Jie Zhou

Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective (2024.findings-acl)

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.

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Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Saliency as Evidence: Event Detection with Trigger Saliency Attribution (2022.acl-long)

Challenge:	Existing studies decompose complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants.
Approach:	They propose to use code comments as natural logic pivot between natural language and code language to boost the code generation ability of code LLMs.
Outcome:	The proposed method significantly improves the code pass rate on humanEval and MBPP, while the robustness of the logical comment decoding strategy is higher than the Chain-of-thoughts prompting.

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Jian Liu, Yufeng Chen, Jinan Xu

Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)

Challenge:	Existing methods to ED see no differences between event types and use a single model to address them all.
Approach:	They propose a new concept termed trigger salience attribution which can explicitly quantify the underlying patterns of events.
Outcome:	The proposed model can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks.

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Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao

Dialogue Systems & Conversational Agents

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.

A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)

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Mingtong Liu, Erguang Yang, Deyi Xiong, Yujie Zhang, Yao Meng, Changjian Hu, Jinan Xu, Yufeng Chen

Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction (2021.emnlp-main)

Challenge:	Paraphrase generation is of great importance for many downstream tasks in natural language processing.
Approach:	They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training.
Outcome:	The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets.

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Jian Liu, Yufeng Chen, Jinan Xu

Reasoning

Challenge:	Existing datasets are too small to train a model for capturing regularities underlying how event arguments are extracted.
Approach:	They propose to bridge implicit EAE with machine reading comprehension (MRC) by building a unified training framework and explicit data augmentation regimes via MRC.
Outcome:	The proposed method obtains state-of-the-art performance on two benchmarks and demonstrates superior results in a data-low scenario.

Can Multi-agent Help Disambiguation in Multi-domain Translation? (2026.findings-acl)

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Zhibo Man, Shaoyang Xu, Yujie Zhang, Yi Feng, Yuanmeng Chen, Yufeng Chen, Xu Jinan, Wenxuan Zhang

Machine Translation

Challenge:	Existing multi-agent systems have shown strong potential for machine translation (MT) but their performance in multidomain translation remains unsatisfactory due to cross-domain word ambiguity .
Approach:	They propose a multi-agent collaborative disambiguation framework for MDT that leverages the collaborative capabilities of LLMs for disambiguations.
Outcome:	The proposed framework improves translation performance across multiple domains and improves disambiguation accuracy.

MSCTD: A Multimodal Sentiment Chat Translation Dataset (2022.acl-long)

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Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

Natural Language Interfaces

Challenge:	Multimodal machine translation and textual chat translation have received considerable attention . however, little research has been devoted to multimodal machine translator in conversations .
Approach:	They propose a task to generate more accurate translations with the help of dialogue history and visual context.
Outcome:	The proposed task can generate more accurate translations with the help of dialogue history and visual context.

A Holistic Approach to Reference-Free Evaluation of Machine Translation (2023.acl-short)

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Hanming Wu, Wenjuan Han, Hui Di, Yufeng Chen, Jinan Xu

LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)

Challenge:	Traditional machine translation evaluation relies on reference written by humans . reference-free evaluation gets rid of labor-intensive annotations, which can pivot easily to new domains .
Approach:	They propose a reference-free evaluation approach that characterizes evaluation as two aspects: fluency and faithfulness.
Outcome:	The proposed approach outperforms SOTA reference-fee metrics on machine translation datasets.

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Zengkui Sun, Yijin Liu, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

Modeling Bilingual Conversational Characteristics for Neural Chat Translation (2021.acl-long)

Challenge:	Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue.
Approach:	They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder.
Outcome:	The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets.

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Yunlong Liang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Confidence-Aware Scheduled Sampling for Neural Machine Translation (2021.findings-acl)

Challenge:	Neural chat translation aims to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency.
Approach:	They propose to model the translation quality of conversational text by learning distributions of bilingual conversational characteristics.
Outcome:	The proposed approach outperforms baseline models and is widely available.

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Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)

Challenge:	Neural machine translation models are trained to maximize the likelihood of the next token given previous golden tokens as inputs, but at the inference stage, golden token is unavailable.
Approach:	They propose a scheduled sampling method that randomly replaces groundtruth tokens with predicted ones during training, ignoring real-time model competence.
Outcome:	The proposed method outperforms the Transformer and vanilla scheduled sampling on large-scale translations.

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Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu

Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition (2021.findings-acl)

Challenge:	Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario.
Approach:	They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus.
Outcome:	The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge.

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Ying Zhang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

CM-Align: Consistency-based Multilingual Alignment for Large Language Models (2025.findings-emnlp)

Challenge:	Named entity recognition (NER) is one of the fundamental tasks in natural language processing.
Approach:	They propose four practical guidelines to guide knowledge transfer and task finetuning . they propose a framework to exploit data from three aspects in a unified training manner .
Outcome:	The proposed framework improves on six benchmarks and shows that it is state-of-the-art in five languages.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment (2022.emnlp-main)

Challenge:	Current large language models (LLMs) show a significant performance gap in alignment between English and other languages.
Approach:	They propose a consistency-based method to construct high-quality multilingual preference data for improving multilingual alignment.
Outcome:	The proposed method is based on three LLMs and three common tasks and shows that it performs better than current methods.

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Siyu Lai, Zhen Yang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching (D19-1)

Challenge:	Existing word alignment models capture few interactions between input sentence pairs, which severely degrades the word alignment quality.
Approach:	They propose to model deep interactions between input and target sentences using a two-stage training framework to train the model.
Outcome:	The proposed model achieves the state-of-the-art (SOTA) performance on four out of five language pairs.

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Mingtong Liu, Yujie Zhang, Jinan Xu, Yufeng Chen

Challenge:	Sentence matching is a key issue in natural language inference and paraphrase identification.
Approach:	They propose a semantics-oriented attention and deep fusion network (OSOA-DFN) that is oriented to the original semantic representation of another sentence and propagates attention information at each matching layer.
Outcome:	The proposed model can model sentence matching more precisely on three sentence matching benchmark datasets.

Outdated Issue Aware Decoding for Factual Knowledge Editing (2024.findings-acl)

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Zengkui Sun, Yijin Liu, Jiaan Wang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

Reasoning

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.

Structure and Label Constrained Data Augmentation for Cross-domain Few-shot NER (2023.findings-emnlp)

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Jingyi Zhang, Ying Zhang, Yufeng Chen, Jinan Xu

Low-Resource NLP

Challenge:	Named entity recognition (NER) tasks require large datasets with accurate annotations that are labor-intensive and time-consuming.
Approach:	They propose a method to leverage domain gaps to model cross-domain few-shot named entity recognition (NER) NER is a natural language processing task to detect entity mentions and classify them into predefined labels .
Outcome:	The proposed method achieves state-of-the-art or competitive results on standard datasets.

Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation (2022.naacl-main)

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Siyu Lai, Zhen Yang, Fandong Meng, Xue Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

Robustness in NLP

Challenge:	Existing approaches to generate adversarial examples for NMT use the meaning-preserving restriction.
Approach:	They propose a new definition for adversarial examples based on the Doubly Round-Trip Translation (DRTT) they introduce masked language models to construct bilingual adversarials based upon DRTT .
Outcome:	The proposed approach significantly improves the robustness of the NMT model on clean and noisy test sets.

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

Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)

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.

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Bo Chen, Xiaotao Gu, Yufeng Hu, Siliang Tang, Guoping Hu, Yueting Zhuang, Xiang Ren

Multilingual Knowledge Editing with Language-Agnostic Factual Neurons (2025.coling-main)

Challenge:	Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself.
Approach:	They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet.
Outcome:	The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

Knowledge Representation

Challenge:	Existing methods to update factual knowledge overlook connections of same knowledge between different languages, resulting in knowledge conflicts and limited edit performance.
Approach:	They propose a method to edit multilingual knowledge simultaneously that avoids knowledge conflicts and improves edit performance.
Outcome:	The proposed method avoids knowledge conflicts and improves edit performance on bi-ZsRE and MzsRE benchmarks.

A Quality-based Syntactic Template Retriever for Syntactically-Controlled Paraphrase Generation (2023.emnlp-main)

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Xue Zhang, Songming Zhang, Yunlong Liang, Yufeng Chen, Jian Liu, Wenjuan Han, Jinan Xu

Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation (2022.coling-1)

Challenge:	Existing syntactically-controlled paraphrase generation models perform well with human-annotated or well-chosen syntaktic templates.
Approach:	They propose a quality-based Syntactic Template Retriever to retrieve templates based on the quality of the to-be-generated paraphrases.
Outcome:	The proposed algorithm can generate high-quality paraphrases without sacrificing quality.

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Hongxiao Zhang, Hui Huang, Jiale Gao, Yufeng Chen, Jinan Xu, Jian Liu

Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)

Challenge:	Existing back-translation methods focus on in-domain lexical knowledge, which may lead to poor translation of unseen in- domain words.
Approach:	They propose an iterative constrained back-translation method to incorporate in-domain lexical knowledge into synthetic parallel data from BT.
Outcome:	The proposed method improves the BLEU score by up to 3.08 on four domains.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou

Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data (2021.emnlp-main)

Challenge:	Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English.
Approach:	They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy.
Outcome:	The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath).

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Erguang Yang, Mingtong Liu, Deyi Xiong, Yujie Zhang, Yao Meng, Changjian Hu, Jinan Xu, Yufeng Chen

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Existing studies on syntactically controlled paraphrase generation rely on large-scale parallel data.
Approach:	They propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder which can generate texts in a specified syntastic structure.
Outcome:	The proposed model can generate diverse paraphrases with specified syntactic structure using non-parallel data.

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Rui Qi, Zhibo Man, Yufeng Chen, Fengran Mo, Jinan Xu, Kaiyu Huang

CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding (D19-1)

Challenge:	Large Language Models struggle with multilingual reasoning tasks due to resource constraints . a training-free method improves performance on multilingual thinking tasks .
Approach:	They propose a training-free method that transforms language-specific semantic information into language-agnostic structured representations.
Outcome:	The proposed method outperforms strong baselines on multilingual reasoning tasks.

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Yijin Liu, Fandong Meng, Jinchao Zhang, Jie Zhou, Yufeng Chen, Jinan Xu

Challenge:	Existing models for slot filling and intent detection fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance.
Approach:	They propose a novel Collaborative Memory Network (CM-Net) that captures slot-specific and intent-specific features in a collaborative manner.
Outcome:	The proposed network outperforms existing models on two benchmarks and a self-collected corpus.

A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)

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Hongxiao Zhang, Mingtong Liu, Chunyou Li, Yufeng Chen, Jinan Xu, Ming Zhou

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

Challenge:	Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge .
Approach:	They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data.
Outcome:	The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge.

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Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

Information Retrieval

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.

Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs (2023.emnlp-main)

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Jian Liu, Weichang Liu, Yufeng Chen, Jinan Xu, Zhe Zhao

Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization (2023.acl-long)

Challenge:	Named entity recognition datasets are notorious for their noisy nature due to annotation errors, inconsistencies, and subjective interpretations.
Approach:	They propose a method that considers NER as a constituency tree parsing problem and uses a tree-structured Conditional Random Fields with uncertainty evaluation for integration.
Outcome:	The proposed model exhibits superb performance even in extreme scenarios with 90% annotation noise.

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Yunlong Liang, Fandong Meng, Jinan Xu, Jiaan Wang, Yufeng Chen, Jie Zhou

Multimodality

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.

Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts (2025.acl-long)

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

Green & Sustainable NLP

Challenge:	Existing large language models (LLMs) have remarkable ability in high-resource languages, but their performance in multilingual scenarios is still limited.
Approach:	They propose a layer-wise expert allocation algorithm to determine the appropriate number of new experts for each layer.
Outcome:	The proposed method outperforms the previous state-of-the-art baseline with 60% fewer experts in the single-expansion setting and 33.3% fewer in the lifelong-expanding setting.

ICL: Iterative Continual Learning for Multi-domain Neural Machine Translation (2024.findings-emnlp)

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Zhibo Man, Kaiyu Huang, Yujie Zhang, Yuanmeng Chen, Yufeng Chen, Jinan Xu

Machine Translation

Challenge:	Existing studies have focused on learning domain knowledge from multiple domains, but task-specific parameters hinder mutual transfer of knowledge between new domains.
Approach:	They propose an iterative Continual learning framework for multi-domain neural machine translation that leverages previously acquired domain knowledge.
Outcome:	The proposed model outperforms baseline models on UM-Corpus and OPUS datasets.

Scheduled Sampling Based on Decoding Steps for Neural Machine Translation (2021.emnlp-main)

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Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Mitigating Language-Level Performance Disparity in mPLMs via Teacher Language Selection and Cross-lingual Self-Distillation (2024.naacl-long)

Challenge:	Neural machine translation models are trained to maximize the likelihood of next token given previous golden tokens as inputs, but at the inference stage, golden token is unavailable.
Approach:	They propose to use scheduled sampling to replace ground-truth tokens with predicted tokens to bridge the gap between training and inference.
Outcome:	The proposed methods outperform the Transformer baseline and vanilla scheduled sampling on three large-scale WMT tasks.

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Haozhe Zhao, Zefan Cai, Shuzheng Si, Liang Chen, Yufeng He, Kaikai An, Baobao Chang

AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation (2025.acl-long)

Challenge:	Large-scale multilingual pretrained language models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different languages within the same mPLm.
Approach:	They propose to leverage the learned knowledge from well-performing languages to guide under-performing ones within the same mPLM.
Outcome:	The proposed model shows that it can guide under-performing languages while minimizing language-level performance disparities across different mPLMs.

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Songming Zhang, Xue Zhang, Tong Zhang, Bojie Hu, Yufeng Chen, Jinan Xu

Semantic Text Processing

Challenge:	Existing methods for LLM alignment optimize tokens using a sparse, response-level reward or preference annotation.
Approach:	They propose an RLHF-equivalent distillation method for token-level reward optimization that incorporates the reward learned by DPO into the RLHG objective and builds a token-based teacher distribution.
Outcome:	The proposed method bridges the accuracy gap between the reward from the DPO model and the pure reward model by building a contrastive DPO reward with a normal and a reverse DPO.

An Iterative Associative Memory Model for Empathetic Response Generation (2024.acl-long)

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Zhou Yang, Zhaochun Ren, Wang Yufeng, Haizhou Sun, Chao Chen, Xiaofei Zhu, Xiangwen Liao

Dialogue Systems & Conversational Agents

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.

MT2: Towards a Multi-Task Machine Translation Model with Translation-Specific In-Context Learning (2023.emnlp-main)

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Chunyou Li, Mingtong Liu, Hongxiao Zhang, Yufeng Chen, Jinan Xu, Ming Zhou

Low-Resource NLP

Challenge:	Sentence-level translation, document-level and terminology constrained translations are important in machine translation.
Approach:	They propose a multi-task machine translation model that integrates translation memory sentences . they propose 'in-context learning' paradigm that allows translation-specific context learning .
Outcome:	The proposed model improves translation memory, document-level translation, and document-constrained translation tasks.

A Multi-modal Debiasing Model with Dynamical Constraint for Robust Visual Question Answering (2023.findings-acl)

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Yu Li, Bojie Hu, Fengshuo Zhang, Yahan Yu, Jian Liu, Yufeng Chen, Jinan Xu

Adversarially Improving NMT Robustness to ASR Errors with Confusion Sets (2022.aacl-short)

Challenge:	Recent studies have shown that many well-developed Visual Question Answering systems suffer from bias problem.
Approach:	They propose a way to mitigate bias problem by subtracting bias score from standard VQA base score.
Outcome:	The proposed method improves on the VQA v2.0 and VQA-CP V2,0 datasets.

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Shuaibo Wang, Yufeng Chen, Songming Zhang, Deyi Xiong, Jinan Xu

An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis (2021.findings-emnlp)

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.

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Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

Sentiment Analysis

Challenge:	Existing approaches to Aspect-based sentiment analysis do not exploit the interactive relations among subtasks and do not utilize document-level labeled domain/sentiment knowledge, which restricts their performance.
Approach:	They propose an iterative multi-knowledge transfer network for end-to-end ABSA that leverages the inter-task interaction between subtasks.
Outcome:	The proposed approach improves on three benchmark datasets.

Long Text Generation with Topic-aware Discrete Latent Variable Model (2022.emnlp-main)

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Erguang Yang, Mingtong Liu, Deyi Xiong, Yujie Zhang, Yufeng Chen, Jinan Xu

Scheduled Multi-task Learning for Neural Chat Translation (2022.acl-long)

Challenge:	Recent work focuses on the modeling of discourse relation, resulting in discrete codes learning shallow semantics.
Approach:	They propose a topic-aware latent code-guided text generation model that encourages discrete codes to model information about topics.
Outcome:	The proposed model generates more topic-relevant and coherent texts.

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Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou

Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)

Challenge:	Existing methods to improve chat translation focus on modeling bilingual dialogue characteristics . however, there is insufficient data and simple joint training methods .
Approach:	They propose a scheduled multi-task learning framework to incorporate large-scale chat translation data into training by adding a second pre-training stage between the original pre- training and fine-tuning stages.
Outcome:	The proposed framework improves translation tasks on four language directions.

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Wenbin Jiang, Mengfei Guo, Yufeng Chen, Ying Li, Jinan Xu, Yajuan Lyu, Yong Zhu

GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)

Challenge:	Existing knowledge graph completion models only evaluated candidate triples from content information.
Approach:	They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation.
Outcome:	The proposed model improves on two representative datasets and improves performance.

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Yijin Liu, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie Zhou

Chunking

Challenge:	Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information.
Approach:	They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations .
Outcome:	The proposed architecture outperforms the best reported results on two standard sequence labeling tasks.

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

Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification (2021.findings-emnlp)

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.

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Siyu Lai, Hui Huang, Dong Jing, Yufeng Chen, Jinan Xu, Jian Liu

Dual-Space Knowledge Distillation for Large Language Models (2024.emnlp-main)

Challenge:	Recent multilingual pre-trained models have been demonstrated effective in many cross-lingual tasks.
Approach:	They propose a framework that leverages code-switched data with multi-view learning to fine-tune XLM-R.
Outcome:	The proposed model achieves state-of-the-art on zero-shot cross-lingual sentiment classification and dialogue state tracking tasks.

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Songming Zhang, Xue Zhang, Zengkui Sun, Yufeng Chen, Jinan Xu

Learning Structural Information for Syntax-Controlled Paraphrase Generation (2022.findings-naacl)

Challenge:	Existing large language models (LLMs) have strong generalization abilities due to their huge model capacities.
Approach:	They propose a dual-space knowledge distillation framework that unifies the output spaces of the two models for KD.
Outcome:	The proposed framework outperforms existing white-box KD frameworks on task-agnostic instruction-following benchmarks and can automatically align representations of two models with different vocabularies.

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Erguang Yang, Chenglin Bai, Deyi Xiong, Yujie Zhang, Yao Meng, Jinan Xu, Yufeng Chen