Papers by Zhirui Zhang
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)
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Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Guoping Huang, Shuming Shi, Jiajun Chen, Shujian Huang
| Challenge: | Existing systems that use a left-to-right completion paradigm are inefficient and expensive. |
| Approach: | They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation . |
| Outcome: | The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems . |
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer (2023.emnlp-main)
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| Challenge: | Nearest Neighbor Machine Translation (kNN-MT) is a powerful domain adaptation tool . the reasons for its success have not been thoroughly investigated . |
| Approach: | They propose to integrate pre-trained Neural Machine Translation models with token-level retrieval . they propose to implicitly execute gradient descent on the output projection layer of NMT . |
| Outcome: | The proposed approach outperforms model fine-tuning on in-domain tests while achieving better performance on out-of-domain sets. |
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)
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Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu
| Challenge: | Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty. |
| Approach: | They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges. |
| Outcome: | The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods. |
Generative Bridging Network for Neural Sequence Prediction (N18-1)
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| Challenge: | Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing. |
| Approach: | They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence. |
| Outcome: | The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden. |
Adaptive Nearest Neighbor Machine Translation (2021.acl-short)
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| Challenge: | kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Approach: | They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Outcome: | The proposed method outperforms the existing model on four benchmark datasets and is open-source. |
CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)
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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination. |
| Approach: | They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns. |
| Outcome: | The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages. |
Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation (2024.findings-acl)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) can be used as translation evaluators. |
| Approach: | They propose to use both coarse-grained and fine-grounded prompts to discern the utility of source versus reference data in machine translation evaluation tasks. |
| Outcome: | The proposed model can be used to evaluate translations in multiple languages. |
Document-Level Machine Translation with Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks. |
| Approach: | They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4. |
| Outcome: | The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation. |
Simple-VGC: Enhancing Visual Grounding in Multimodal Reasoning via Adaptive Tool Composition (2026.acl-long)
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| Challenge: | Existing multimodal large language models suffer from systematic failures in basic visual understanding. |
| Approach: | They propose a tool-augmented reasoning framework with three targeted compensation strategies to address these problems. |
| Outcome: | The proposed framework improves visual grounding by re-injecting the original image to mitigate visual forgetting, the authors show . the proposed framework also improves the accuracy of the visual inputs, the researchers show - and the results are promising . |
S-RAG: A Novel Audit Framework for Detecting Unauthorized Use of Personal Data in RAG Systems (2025.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems rely on external data for accurate and context-specific responses. |
| Approach: | They propose a framework that enables users to determine whether their textual data has been utilized in RAG systems even in black-box settings with no prior system knowledge. |
| Outcome: | The proposed framework achieves an improvement in Accuracy by 19.9% while maintaining strong performance under adversarial defenses. |
Iterative Domain-Repaired Back-Translation (2020.emnlp-main)
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| Challenge: | Existing studies show that NMT models perform poorly in specific domains when in-domain parallel corpora are scarce or nonexistent. |
| Approach: | They propose an iterative domain-repaired back-translation framework to refine translations in bilingual data by round-trip translating monolingual sentences. |
| Outcome: | The proposed framework achieves 15.79 and 4.47 BLEU improvements over unadapted models and back-translation in domain-specific translations. |
Automatic Song Translation for Tonal Languages (2022.findings-acl)
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| Challenge: | Existing automatic song translation systems for tonal languages do not match the number of notes and beat the original rhythm of the song. |
| Approach: | They propose three criteria for effective AST: preserving meaning, singability and intelligibility. |
| Outcome: | The proposed system balances semantics and singability with human evaluations. |
Rethinking Word-Level Auto-Completion in Computer-Aided Translation (2023.emnlp-main)
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| Challenge: | Existing models for word-level auto-completion (WLAC) do not meet the criterion of good auto-completes. |
| Approach: | They propose a measurable criterion to address the question: what kind of words are good auto-completions? they propose an approach to enhance WLAC performance by promoting adherence to the cri-terion. |
| Outcome: | The proposed approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022 while using significantly smaller model sizes. |
Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)
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| Challenge: | End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters. |
| Approach: | They propose a non-parametric method that leverages in-domain text translation corpus to achieve domain adaptation for E2E-ST systems. |
| Outcome: | The proposed method outperforms the existing in-domain fine-tuning strategies on the Europarl-ST benchmark. |
Budgeted Policy Learning for Task-Oriented Dialogue Systems (P19-1)
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| Challenge: | Existing approaches to integrating reinforcement learning into task-oriented dialogue systems require a fixed, small amount of user interactions to learn. |
| Approach: | They propose a budget-conscious scheduling approach that optimizes a fixed, small amount of user interactions for dialogue agent learning. |
| Outcome: | The proposed approach improves on a movie-ticket booking task with simulated and real users. |
Learning to Collaborate for Question Answering and Asking (N18-1)
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| Challenge: | Question answering (QA) and question generation (QG) are closely related tasks. |
| Approach: | They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario. |
| Outcome: | The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario. |
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)
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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions. |
| Approach: | They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level. |
| Outcome: | The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods. |
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables (2021.findings-emnlp)
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| Challenge: | Existing methods to achieve zero-shot translation suffer from spurious correlations between output language and language invariant semantics. |
| Approach: | They propose a method that denoizes the autoencoder objective based on pivot language into traditional training objective to improve translation accuracy on zero-shot directions. |
| Outcome: | The proposed method eliminates spurious correlations and outperforms state-of-the-art methods on two benchmark machine translation datasets. |
Rethinking Translation Memory Augmented Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing approaches to enhance neural machine translation (NMT) by using a TM have been reported to be effective. |
| Approach: | They propose a translation memory augmented neural machine translation model that is good at fitting data but more sensitive to fluctuations in training data. |
| Outcome: | The proposed model achieves consistent gains over conventional and existing models under two variance-preferable scenarios as well as the high resource scenario. |
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc. |
| Approach: | They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval. |
| Outcome: | The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation. |