Papers by Jiali Zeng
Soft Language Clustering for Multilingual Model Pre-training (2023.acl-long)
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| Challenge: | Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from the source language or when pre-training data is limited in size. |
| Approach: | They propose a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally. |
| Outcome: | The proposed method improves on the XTREME task and also for low-resource languages in unsupervised sentence retrieval. |
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding (2022.findings-emnlp)
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| Challenge: | Recent studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. |
| Approach: | They propose a novel Contrastive learning method with Prompt-derived Virtual semantic prototypes that constructs virtual semantic prototype to each instance and derives negative prototypes by using the negative form of the prompts. |
| Outcome: | The proposed method performs on semantic textual similarity, transfer, and clustering tasks compared to baselines. |
Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)
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Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu
| Challenge: | Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers. |
| Approach: | They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights. |
| Outcome: | The proposed model reduces redundancy by sharing weights across layers while maintaining high response quality while reducing redundant calculations within 53% 84% of the total layers. |
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing approaches to named entity recognition (NER) are limited to high-resource languages like English and Chinese. |
| Approach: | They propose a framework to make full use of annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition. |
| Outcome: | The proposed framework makes full use of both annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). |
Type-Driven Multi-Turn Corrections for Grammatical Error Correction (2022.findings-acl)
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| Challenge: | Existing studies focus on data augmentation to combat exposure bias . but data augmented models lack the ability to recognize the procedure of gradual corrections . |
| Approach: | They propose a type-driven multi-turn corrections approach that uses multiple training instances to train dominant models. |
| Outcome: | The proposed model achieves state-of-the-art single-model performance on English GEC benchmarks. |
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025.emnlp-main)
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Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Guanbo Wang, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang
| Challenge: | Existing methods for fine-tuning-based compression suffer from verbose outputs, increasing computational overhead. |
| Approach: | They propose a framework to generate concise reasoning chains using Confidence Injection and Early Stopping. |
| Outcome: | The proposed framework reduces the length of the model by up to 50% while maintaining high task accuracy. |
Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)
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| Challenge: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework. |
| Approach: | They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer. |
| Outcome: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework. |
Recurrent Attention for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent research questions the importance of dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. |
| Approach: | They propose a novel mechanism to replace dot-product self-attention with a recurrent atteNtion mechanism that directly learns attention weights without token-to-token interaction. |
| Outcome: | The proposed model outperforms the Transformer model on translation tasks with fewer parameters and inference time. |
LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information (2025.findings-acl)
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| Challenge: | Recent advances in large language models have improved their capacity to handle long text inputs, but current models still exhibit unsatisfactory performance in long-form generation. |
| Approach: | They propose a method to enhance long-form text generation through step-level supervision by leveraging Monte Carlo Tree Search to collect stepwise preference pairs and employ a global memory pool to maintain factual accuracy. |
| Outcome: | The proposed method improves performance on long-form generation benchmarks while maintaining lossless performance on several general benchmarks. |
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
| Approach: | They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. |
| Outcome: | The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks. |
Trust in Internal or External Knowledge? Generative Multi-Modal Entity Linking with Knowledge Retriever (2024.findings-acl)
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| Challenge: | Existing generative approaches struggle with the knowledge gap between visual entity information and the intrinsic parametric knowledge of LLMs. |
| Approach: | They propose a knowledge retrieval method that leverages external sources to enhance visual entity information and a prioritization scheme that handles noisy retrieval results. |
| Outcome: | The proposed method shows improvements of 3.0% to 6.5% across all evaluation metrics compared to baselines. |
Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding (2024.findings-acl)
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| Challenge: | Commercial machine translation engines are proficient in addressing the majority of translation requirements. |
| Approach: | They propose to combine NMT and MT-oriented LLMs to achieve superior translation quality by combining their strengths. |
| Outcome: | The proposed model can handle complex scenarios beyond the capability of NMT alone. |
Learning Confidence for Transformer-based Neural Machine Translation (2022.acl-long)
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| Challenge: | A well-calibrated confidence estimate is not sufficient for neural machine translation (NMT) where probabilities from softmax distribution fail to describe when the model is probably mistaken. |
| Approach: | They propose an unsupervised confidence estimate learning jointly with the training of a neural machine translation model to quantify confidence. |
| Outcome: | The proposed model outperforms standard label smoothing and can predict failures in two real-world scenarios. |
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective (2024.acl-long)
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| Challenge: | Neural Machine Translation (NMT) has made remarkable progress over the past years, but under-translation and over-translatation remain challenging obstacles faced by NMT systems. |
| Approach: | They propose to employ the confidence of predicting the end of sentence (EOS) as a detector for under-translation and strengthen the confidence-based penalty to penalize candidates with a high risk of under-translated. |
| Outcome: | The proposed method can detect and rectify under-translated outputs, with minor impact on other correct translations. |
Task-guided Disentangled Tuning for Pretrained Language Models (2022.findings-acl)
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| Challenge: | Pretrained language models are fine-tuned on task-specific datasets, but fail to capture task- specific patterns. |
| Approach: | They propose a method which disentangles task-relevant signals from entangled representations. |
| Outcome: | The proposed method improves generalization of representations by disentangling task-relevant signals from the entangled representations. |
Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning (2026.findings-acl)
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| Challenge: | Large language models generate costly yet semantically void reasoning on beyond-capability tasks . the dominant failure mode is specious reasoning, superficially valid outputs with subtle hallucinations . |
| Approach: | They propose a capability-aligned reinforcement learning approach that aligns model behavior with capability boundaries. |
| Outcome: | The proposed model reduces futile reasoning while maintaining performance across tasks. |
TasTe: Teaching Large Language Models to Translate through Self-Reflection (2024.acl-long)
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| Challenge: | Existing approaches to enhance LLMs' performance in machine translation are unable to fully exploit their instruction-following capabilities. |
| Approach: | They propose a framework for translating through self-reflection that involves two stages of inference . they propose to use the framework to refine LLMs' preliminary translations . |
| Outcome: | The proposed framework can produce translation outputs that match the quality of NMT systems. |
Joint Optimization of Training Data and Policy in RLHF (2026.findings-acl)
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Zhuohao Yu, Jiali Zeng, Weizheng Gu, Mengyuan Sun, Yidong Wang, Fandong Meng, Jie Zhou, Shikun Zhang, Wei Ye
| Challenge: | JODP optimizes policies on fixed training inputs, limiting the diversity of learning signals. |
| Approach: | They propose a framework where policy generates improved variants of training problems to enhance its own learning. |
| Outcome: | The proposed framework improves on safety alignment tasks by allowing 4B models to reach 8B model performance with less than 1% additional computational overhead. |
LexMatcher: Dictionary-centric Data Curation for LLM-based Machine Translation (2024.findings-emnlp)
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| Challenge: | emergence of large language models (LLMs) has brought about new opportunities for machine translation. |
| Approach: | They propose a method for data curation that supplements the infrequent senses of polysemous words. |
| Outcome: | The proposed method outperforms established baselines on the WMT2022 test sets and is applicable to other pre-trained models. |
Consistency Regularization Training for Compositional Generalization (2023.acl-long)
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| Challenge: | Existing neural models have difficulty generalizing to unseen combinations of seen components. |
| Approach: | They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures. |
| Outcome: | The proposed model performs well on semantic parsing and machine translation benchmarks. |
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension (2020.coling-main)
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| Challenge: | Experimental results show that our model achieves state-of-the-art performance for Chinese idiom comprehension. |
| Approach: | They propose a model that can mitigate the inconsistency between literal and literal meanings by incorporating the synonym knowledge enhanced reader into the model. |
| Outcome: | The proposed model achieves state-of-the-art on a Chinese idiom reading comprehension dataset. |
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)
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Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su
| Challenge: | Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. |
| Approach: | They propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. |
| Outcome: | The proposed model achieves state-of-the-art performance on five commonly-used datasets. |
Attention Calibration for Transformer in Neural Machine Translation (2021.acl-long)
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| Challenge: | Attention mechanisms have been ubiquitous in neural machine translation (NMT) however, many studies doubt whether highlyattended inputs have a large impact on the model outputs. |
| Approach: | They propose to introduce a mask perturbation model that automatically evaluates each input’s contribution to the model outputs. |
| Outcome: | The proposed model is more uniform at lower layers while more concentrated on the specific inputs at higher layers. |
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks (2022.coling-1)
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| Challenge: | Existing unsupervised summarization methods fail to consider efficiency and effectiveness when the input document is extremely long. |
| Approach: | They propose an efficient Coarse-to-Fine Facet-Aware Ranking framework for unsupervised long document summarization based on the semantic block. |
| Outcome: | The proposed framework can achieve new state-of-the-art unsupervised summarization results on Gov-Report, billSum, arXiv, and PubMed. |