Papers by Tiejun Zhao

Cross Copy Network for Dialogue Generation (2020.emnlp-main)

Challenge:	Existing studies show that the ability of large language models to generate contextual understanding of the sentence can degrade translation quality.
Approach:	They propose a method that generates contextual understanding for both source and target languages separately.
Outcome:	The proposed method outperforms strong comparison methods in multiple domains.

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Changzhen Ji, Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Conghui Zhu, Tiejun Zhao

Dialogue Systems & Conversational Agents

Challenge:	Despite the success of sequence-to-sequence models, dialogue logics are often ignored.
Approach:	They propose a network architecture to explore the current dialog context and similar dialogue instances’ logical structure simultaneously.
Outcome:	The proposed network architecture is superior to existing state-of-the-art models.

Robust Machine Reading Comprehension by Learning Soft labels (2020.coling-main)

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Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, Tiejun Zhao

Reasoning

Challenge:	Neural models have achieved great success on the task of machine reading comprehension, which are typically trained on hard labels.
Approach:	They propose a robust training method for machine reading comprehension models to address label sparseness problem by using three strategies to train models on soft labels.
Outcome:	The proposed method improves the baseline model performance and achieves state-of-the-art performance on NewsQA and QUOREF.

Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation (2025.acl-long)

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Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang

Multimodality

Challenge:	Experimental results show that our model significantly outperforms existing multimodal MT and text-only MT.
Approach:	They propose a stable diffusion-based imagination network into a multimodal large language model to generate an image for each source sentence.
Outcome:	The proposed model outperforms existing multimodal and text-only MT and achieves an average improvement of 14 BLEU points on Multi30K and MSCOCO multimodal MT benchmarks.

A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)

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Xinpeng Liu, Bing Xu, Muyun Yang, Hailong Cao, Conghui Zhu, Tiejun Zhao, Wenpeng Lu

Low-Resource NLP

Challenge:	Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs.
Approach:	They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation .
Outcome:	The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets.

DKME: Rethinking Coupled Knowledge Memory for Lifelong Model Editing of Large Language Models (2026.findings-acl)

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Guanyu Zheng, Wang Zhenyu, He Tingting, Xv Wang, Haochang Wang, Yaokai Huang, Tiejun Zhao

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4 (2025.findings-acl)

Challenge:	Existing memory-based editors suffer from catastrophic forgetting as edits accumulate.
Approach:	They propose a method which injects factual updates into large language models without retraining or finetuning into existing memory-based editors.
Outcome:	Experiments on HalluEditBench, CKnowEdit, and WikiDatacounterfact show that the proposed model achieves a more favorable trade-off between editing success and locality compared to baselines while maintaining more stable performance as the edit scale increases.

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Hui Huang, Xingyuan Bu, Hongli Zhou, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao

Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)

Challenge:	Recent studies have fine-tuned judge models based on open-source LLMs to evaluate the quality of other LLM.
Approach:	They propose to use open-source LLMs to evaluate Large Language Models (LLMs) their empirical results show that the models underperform GPT-4 in several dimensions .
Outcome:	The proposed models outperform GPT-4 on several dimensions including generalizability, fairness and adaptability.

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Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

Sentence-Level Agreement for Neural Machine Translation (P19-1)

Challenge:	Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence.
Approach:	They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model.
Outcome:	The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU.

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Mingming Yang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Min Zhang, Tiejun Zhao

Document-Level Relation Extraction with Sentences Importance Estimation and Focusing (2022.naacl-main)

Challenge:	Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT)
Approach:	They propose a sentence-level agreement module to minimize the difference between the representation of source and target sentences.
Outcome:	Empirical results show the proposed agreement module significantly improves translation performance.

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Wang Xu, Kehai Chen, Lili Mou, Tiejun Zhao

UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)

Challenge:	Document-level relation extraction models are not robust and exhibit bizarre behaviors when non-evidence sentences are removed.
Approach:	They propose a document-level relation extraction framework that uses a sentence importance score and a focusing loss to encourage DocRE models to focus on evidence sentences.
Outcome:	The proposed framework improves overall performance and makes DocRE models more robust.

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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao

Multimodality

Challenge:	Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based.
Approach:	They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation.
Outcome:	The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation .

LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation (2025.coling-main)

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Hongyun Zhou, Xiangyu Lu, Wang Xu, Conghui Zhu, Tiejun Zhao, Muyun Yang

Green & Sustainable NLP

Challenge:	Low-Rank Adaptation (LoRA) is currently the most commonly used PEFT method for fine-tuning models with billions of parameters.
Approach:	They propose to use low-rank Adaptation to evaluate LoRA parameter features and then retain LoRA for important layers and the other layers share the same LoRA.
Outcome:	The proposed method achieves comparable performance to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average.

Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)

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Yingyao Wang, Junwei Bao, Guangyi Liu, Youzheng Wu, Xiaodong He, Bowen Zhou, Tiejun Zhao

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)

Challenge:	Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations .
Approach:	They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations.
Outcome:	The proposed model achieves comparable and even better results to strong baselines in terms of accuracy.

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Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao

Improving Low-resource Question Answering by Augmenting Question Information (2023.findings-emnlp)

Challenge:	Natural Language Sentence Matching (NLSM) is a popular NLP task.
Approach:	They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework.
Outcome:	The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions.

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Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min Zhang

Low-Resource NLP

Challenge:	Low-resource questions pose a significant challenge within the field of Question-Answering (QA) tasks.
Approach:	They propose a method that leverages large models' internal knowledge to enhance the quality of augmented data by Prompt Answer, Question Generation, and Question Filter.
Outcome:	The proposed method outperforms existing augmentation strategies on high-resource QA tasks like SQUAD1.1 and TriviaQA.

CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection (2022.findings-naacl)

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Zhen Li, Bing Xu, Conghui Zhu, Tiejun Zhao

MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)

Challenge:	Existing methods for multimodal sentiment detection do not consider token-level feature fusion.
Approach:	They propose a method for multimodal sentiment detection using a combination of text and image to encode and fuse token-level features.
Outcome:	The proposed method can fuse multimodal features with token-level features on three publicly available multimodal datasets.

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Hui Huang, Jiaheng Liu, Yancheng He, Shilong Li, Bing Xu, Conghui Zhu, Muyun Yang, Tiejun Zhao

MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering (2022.findings-emnlp)

Challenge:	Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application.
Approach:	They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model.
Outcome:	The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity.

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Yingyao Wang, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao

Structured Data in NLP

Challenge:	Conventional HQA models retrieve coarse- or fine-grained evidence to reason the answer . however, they neglect a more general scenario requiring reasoning over heterogeneous data to answer a question.
Approach:	They propose a multi-granularity evidence retrieval and reasoning approach to answer questions over heterogeneous data using tables and passages linked to table cells.
Outcome:	The proposed approach significantly boosts the performance on the HybridQA dataset.

Iterative Nearest Neighbour Machine Translation for Unsupervised Domain Adaptation (2023.findings-acl)

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Hui Huang, Shuangzhi Wu, Xinnian Liang, Zefan Zhou, Muyun Yang, Tiejun Zhao

Low-Resource NLP

Challenge:	Existing methods for supervised domain adaptation of machine translation focus on fine-tuning, which is non-extensible.
Approach:	They propose to perform unsupervised domain adaptation in a non-parametric manner by using in-domain monolingual data and performing nearest neighbour inference on both forward and backward directions.
Outcome:	The proposed method significantly improves the in-domain translation performance and achieves state-of-the-art results among non-parametric methods.

Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (2026.acl-long)

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Gewen Liang, Mufan Xu, Kehai Chen, Wei Wang, Yuwei Wang, Muyun Yang, Tiejun Zhao, Min Zhang

On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning (2022.naacl-main)

Challenge:	Existing approaches to solve non-deterministic reasoning problems in large language models are limited by their complexity and lack of a clear understanding of the problem.
Approach:	They propose a method to diagnose and correct non-deterministic reasoning behaviors in large language models.
Outcome:	The proposed method outperforms baselines and WebQSP benchmarks on the widely used WebQ SP and CWQ benchmarks.

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Tingting Ma, Qianhui Wu, Zhiwei Yu, Tiejun Zhao, Chin-Yew Lin

Word-level Cross-lingual Structure in Large Language Models (2025.coling-main)

Challenge:	Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem.
Approach:	They propose to modify a few-shot intent detection task to produce a non-trivially strong performance without further domain-specific adaptation.
Outcome:	The proposed model improves on the prototypical network variants with task-specific fine-tuning.

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Zihao Feng, Hailong Cao, Wang Xu, Tiejun Zhao

Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders (2022.findings-naacl)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional performance across a broad spectrum of cross-lingual Natural Language Processing (NLP) tasks.
Approach:	They propose to use Word-level Cross-lingual Structure to prove that the word-level embedding on the hidden layers isomorphic between languages.
Outcome:	The proposed method significantly improves on two representative LLM foundations, LLaMA2 and BLOOM.

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Wang Xu, Tiejun Zhao

Dynamic Planning for LLM-based Graphical User Interface Automation (2024.findings-emnlp)

Challenge:	Existing methods for abstractive summarization generate factual consistency summaries with a high level of accuracy and coherence.
Approach:	They propose a framework that induces the guidance information and generates summary equipment with the guidance synchronously.
Outcome:	The proposed framework generates fluent summaries with no constraint on the words and phrases, and is more faithful than the existing state-of-the-art approaches.

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Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Xinbei Ma, Muyun Yang, Tiejun Zhao, Min Zhang

DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms (2024.acl-short)

Challenge:	Existing approaches to planning for GUI tasks are limited due to long historical dialogues.
Approach:	They propose a novel approach to dynamic planning based on environmental feedback and execution history to guide action prediction in GUI tasks.
Outcome:	The proposed approach surpasses the strong GPT-4V baseline by +12.7% in accuracy.

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Andong Chen, Lianzhang Lou, Kehai Chen, Xuefeng Bai, Yang Xiang, Muyun Yang, Tiejun Zhao, Min Zhang

Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training (2020.coling-main)

Challenge:	Existing self-reflection methods lack effective feedback information, limiting the translation performance of large language models (LLMs).
Approach:	They propose a framework that leverages the dual learning of translation tasks to provide effective feedback, thereby enhancing the models’ self-reflective abilities and improving translation performance.
Outcome:	The proposed framework improves the models’ self-reflective abilities and improves translation accuracy and eliminating ambiguities across translation tasks.

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Haipeng Sun, Rui Wang, Kehai Chen, Xugang Lu, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition (2023.acl-long)

Challenge:	Unsupervised neural machine translation (UNMT) has attracted great interest in the machine translation community.
Approach:	They propose to explicitly take noisy data into consideration to improve the robustness of UNMT based systems.
Outcome:	The proposed methods significantly improved the robustness of the conventional UNMT systems in noisy scenarios.

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Tingting Ma, Qianhui Wu, Huiqiang Jiang, Börje Karlsson, Tiejun Zhao, Chin-Yew Lin

Improving Translation Quality Estimation with Bias Mitigation (2023.acl-long)

Challenge:	Existing approaches to named entity recognition (NER) are limited by the cost of labeling and labeling, especially for low-resource languages.
Approach:	They propose a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other.
Outcome:	The proposed framework achieves superior results on benchmark datasets and can generalize to distant languages.

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Hui Huang, Shuangzhi Wu, Kehai Chen, Hui Di, Muyun Yang, Tiejun Zhao

Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction (2022.coling-1)

Challenge:	State-of-the-art translation Quality Estimation models are biased, relying on monolingual features while ignoring the bilingual semantic alignment.
Approach:	They propose a method to mitigate the bias of translation quality estimation models by contrastive learning between clean and noisy sentence pairs.
Outcome:	The proposed method improves the estimation performance while mitigating the bias.

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Zihao Feng, Hailong Cao, Tiejun Zhao, Weixuan Wang, Wei Peng

Decomposed Meta-Learning for Few-Shot Named Entity Recognition (2022.findings-acl)

Challenge:	Unsupervised bilingual lexicon induction models fail on low-resource language pairs due to insufficient initialization.
Approach:	They propose a method to learn cross-lingual features from monolingual corpora for low-resource UBLI by integrating cross-linguistic representations with pre-trained word embeddings in a fully unsupervised initialization.
Outcome:	The proposed method outperforms state-of-the-art methods on low-resource language pairs and improves representational ability and robustness of existing embedding models.

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Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, Chin-Yew Lin

Understanding and Improving Hidden Representations for Neural Machine Translation (N19-1)

Challenge:	Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples.
Approach:	They propose a decomposed meta-learning approach to solve few-shot span detection and few- shot entity typing problems by introducing a model-agnostic meta-loop algorithm.
Outcome:	The proposed approach achieves superior performance over prior methods on benchmarks.

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Guanlin Li, Lemao Liu, Xintong Li, Conghui Zhu, Tiejun Zhao, Shuming Shi

Discriminative Reasoning for Document-level Relation Extraction (2021.findings-acl)

Challenge:	Existing studies have explored some methods for understanding hidden representations, but they have not sought to improve the translation quality rationally according to their understanding.
Approach:	They propose to construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks.
Outcome:	The proposed methods achieve consistent improvements (up to +1.3 BLEU) on two widely-used datasets.

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Wang Xu, Kehai Chen, Tiejun Zhao

OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)

Challenge:	Existing models use graph networks to implicitly model reasoning skills . but it is yet to be seen whether modeling these reasoning skills implicitly is competitive with intuitive reasoning skills between one entity pair in this document.
Approach:	They propose a discriminative reasoning framework to explicitly model the paths of reasoning skills between entity pairs in a document.
Outcome:	The proposed method outperforms the previous state-of-the-art on the large-scale DocRE dataset.

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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao

Forest-Based Neural Machine Translation (P18-1)

Challenge:	Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability.
Approach:	They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability.
Outcome:	Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA.

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Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, Eiichiro Sumita

Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation (2020.acl-main)

Challenge:	Compared with string-to-string systems, tree-based NMT methods use more syntactic information and can incorporate prior knowledge.
Approach:	They propose a tree-based neural machine translation method that translates a linearized packed forest under a simple sequence-to-sequence framework.
Outcome:	The proposed method outperforms tree-based approaches in the BLEU score of the proposed model.

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Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting (2020.acl-main)

Challenge:	Unsupervised neural machine translation (UNMT) can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time.
Approach:	They propose a method to translate between 13 languages using a single encoder and a decoder . they propose two knowledge distillation methods to further enhance multilingual UNMT performance .
Outcome:	The proposed method improves translation performance for all languages using multilingual data.

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Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, Tiejun Zhao

Text Classification

Challenge:	Recent research has found that text classification datasets contain certain unintended biases, such as text containing demographic identity-terms that are more likely to be abusive.
Approach:	They propose a model-agnostic debiasing framework that recovers the non-discrimination distribution using instance weighting, which does not require extra resources or annotations apart from a pre-defined set of demographic identity-terms.
Outcome:	The proposed framework alleviates the unintended biases without hurting models’ generalization ability.

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios (2021.naacl-main)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

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Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

Neural Document Summarization by Jointly Learning to Score and Select Sentences (P18-1)

Challenge:	Existing methods that use monolingual corpora for translation are not suitable for low-resource languages such as Estonian.
Approach:	They propose unsupervised neural machine translation (UNMT) that relies on monolingual corpora to train a robust UNMT system and improve its performance.
Outcome:	The proposed methods outperform conventional UNMT systems on several language pairs.

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Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, Tiejun Zhao

Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation (P19-1)

Challenge:	Sentence scoring and sentence selection are two main steps in extractive document summarization systems.
Approach:	They propose an end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
Outcome:	The proposed framework outperforms the state-of-the-art summarization models on the CNN/Daily Mail dataset.

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Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

Benchmarking LLMs for Translating Classical Chinese Poetry: Evaluating Adequacy, Fluency, and Elegance (2025.emnlp-main)

Challenge:	Unsupervised bilingual word embedding (UBWE) has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs.
Approach:	They propose two methods that train UNMT with UBWE agreement . they propose to use UBwe to initialize word embedding in UNMT .
Outcome:	The proposed methods outperform conventional methods on several language pairs.

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Andong Chen, Lianzhang Lou, Kehai Chen, Xuefeng Bai, Yang Xiang, Muyun Yang, Tiejun Zhao, Min Zhang

Machine Translation

Challenge:	Existing large language models fall short of translating culturally significant content . existing models fall behind in achieving such translations, authors say .
Approach:	They propose a suitable benchmark for translating classical Chinese poetry into English . they propose RAT, a retrieval-augmented machine translation method that enhances the translation process .
Outcome:	The proposed method improves translation quality in terms of adequate, fluent, and elegant translations.

Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)

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Tingting Ma, Jin-Ge Yao, Chin-Yew Lin, Tiejun Zhao