Papers by Yun Ma
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)
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| Challenge: | End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks. |
| Approach: | They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation. |
| Outcome: | The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference. |
Personality-Guided Code Generation Using Large Language Models (2025.acl-long)
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| Challenge: | Existing studies have shown that personality-guided code generation improves software development outcomes when individuals are assigned tasks that match their personality types. |
| Approach: | They evaluate how emulating personality traits appropriate to the coding tasks affects LLM performance by using seven widely adopted LLMs. |
| Outcome: | The proposed approach improves pass rates in 23 out of 28 LLM-dataset combinations, while emulating personality traits can be easily integrated with other prompting strategies to further boost performance. |
Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders (2021.emnlp-main)
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| Challenge: | Existing work on improving cross-lingual transferability of NMT model is under-explored. |
| Approach: | They propose a model that leverages a multilingual pretrained encoder to improve cross-lingual transferability. |
| Outcome: | The proposed model outperforms mBART and m2m-100 on a zero-shot cross-lingual transfer task. |
Towards Unified Multimodal Large Language Models: A survey (2026.findings-acl)
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| Challenge: | unified multimodal large language models (MLLMs) are emerging but lack a systematic framework to connect them and situate current trends within a broader landscape. |
| Approach: | They present a systematic review of unified Multimodal Large Language Models . they outline the foundational concepts and prerequisites for understanding them . |
| Outcome: | The present review provides a systematic and systematic overview of unified MLLMs . it discusses persistent challenges and identify promising directions for future research . |
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)
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| Challenge: | Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty. |
| Approach: | They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs. |
| Outcome: | Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs). |
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)
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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank
| Challenge: | Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research. |
| Approach: | They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text. |
| Outcome: | The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date. |
Direct Speech-to-Speech Translation With Discrete Units (2022.acl-long)
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Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Sravya Popuri, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu
| Challenge: | Existing direct speech-to-speech translation models rely on text generation as an intermediate step. |
| Approach: | They propose a direct speech-to-speech translation model that translates speech from one language to another without relying on intermediate text generation. |
| Outcome: | The proposed model produces 6.7 BLEUs in the Fisher Spanish-English dataset when trained without any text transcripts and with text supervision. |
DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization (2026.findings-eacl)
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Haiyang Shen, Hang Yan, Zhongshi Xing, Mugeng Liu, Yue Li, Zhiyang Chen, Yuxiang Wang, Jiuzheng Wang, Yun Ma
| Challenge: | Existing retrieval-augmented generation paradigms rely heavily on public knowledge . Existing RAGs reliant on public information and often falter when faced with domain-specific queries. |
| Approach: | They propose a framework that combines a data-construction modeling approach with a scalable synthetic data-generation pipeline to optimize domain-specific retrieval performance. |
| Outcome: | The proposed framework optimizes domain-specific retrieval performance and bolsters retriever robustness. |
Toward Automated Robustness Evaluation of Mathematical Reasoning (2026.findings-acl)
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Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang, Ma Shuguang, Zhaoqian Dai, Hailiang Huang, Yun Chen, Guanhua Chen
| Challenge: | Existing robustness evaluations rely on hand-crafted templates or a limited set of perturbation rules, resulting in model failure. |
| Approach: | They propose a framework inspired by software stress testing that generates adversarial variants via a multi-round rewrite-verify loop, ensuring semantic consistency while successfully inducing model failure. |
| Outcome: | The proposed framework generates adversarial variants dynamically for each LLM, minimizing the risk of data contamination. |
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks (2023.acl-long)
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| Challenge: | Neural based end-to-end frameworks have achieved remarkable success in speech-totext tasks, such as automatic speech recognition (ASR) and speech- totext translation (ST). |
| Approach: | They propose to combine Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks and leverage AED's strength in non-monotonic sequence to sequence learning while retaining Transducers streaming property. |
| Outcome: | The proposed model outperforms Transducer and Attention based Encoder-Decoder (TAED) on the MuST-C dataset and shows that it is not bound by any specific language model. |
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs (2025.acl-long)
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Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang, Mark Harman, Yudong Han, Yun Ma, Yihong Dong, Ge Li, Gang Huang
| Challenge: | TrickCatcher generates test cases that pass existing tests yet contain bugs . a recent study found that tricky bugs are not detected by test suites . |
| Approach: | They propose an LLM-powered approach to generating test cases for uncovering bugs in plausible programs . they use a PUT and specification to generate program variants, an input generator and an Llm to construct test inputs . |
| Outcome: | The proposed approach achieves recall, precision, and F1 scores that are 1.80, 2.65, and 1.66 . trickCatcher generates program variants based on the program under test and its specification . |
Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction. |
| Approach: | This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies . |
| Outcome: | The survey examines the effectiveness of MERC and its evaluation strategies. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
Exploring Non-Autoregressive Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for text style transfer use autoregressive decoding, but they are slow and low parallelizability. |
| Approach: | They propose a base NAR model by directly adapting the common training scheme from its AutoRegressive counterpart. |
| Outcome: | The proposed model sacrifices performance due to lack of conditional dependence between output tokens . knowledge distillation, contrastive learning, and iterative decoding are employed to improve the model . |
MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis (2026.acl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has seen success with English texts, but real-world social media interactions often involve multiple languages. |
| Approach: | They propose a framework for cross-lingual ABSA that incorporates code-switched bilingual sentences into the language discriminator and consistency training modules to enhance cross-linguistic alignment. |
| Outcome: | The proposed framework achieves cross-lingual sentence-level and aspect-level alignment, aligning features of aspect terms in different contextual environments. |
JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning (2025.emnlp-main)
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| Challenge: | Recent studies have introduced legal theories into LLM workflows to improve their understanding of legal texts and reasoning accuracy. |
| Approach: | They evaluate an expert-annotated four-element knowledge base covering 155 criminal charges. |
| Outcome: | The proposed model can be used to analyze criminal charges and retrieve them in legal cases. |
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for unsupervised text style transfer struggle to achieve high style conversion rate and low content loss. |
| Approach: | They propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders. |
| Outcome: | The proposed framework achieves strong empirical results on style compatibility and content preservation. |
Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning (2026.findings-acl)
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| Challenge: | Existing MLLMs are strong at understanding single plots, but struggle with multi-step reasoning . Existing approaches to manage context in chart reasoning include text-based chain-of-thought prompting . |
| Approach: | They propose a hierarchical visual agent framework that iteratively constructs a working context in an image–text space. |
| Outcome: | The proposed framework improves on strong multimodal baselines. |
MEGen: Generative Backdoor into Large Language Models via Model Editing (2025.findings-acl)
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| Challenge: | Existing methods for training large language models are limited to yes-or-no discriminative tasks, leading users to underestimate the potential risks. |
| Approach: | They propose an editing-based generative backdoor that expands the backdoor to generative tasks in a unified format of any text-to-any text. |
| Outcome: | The proposed model achieves high attack success rate by adjusting only a small set of local parameters with few-shot samples. |
CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods to prune redundant vision tokens struggle in shallow layers due to the lack of contextual information. |
| Approach: | They propose a layer-wise contextualized visual token pruning method that uses a plug-and-play Pruning Module to prune redundant vision tokens. |
| Outcome: | The proposed method outperforms training-free pruning methods under equal token budgets and surpasses training based methods with comparable supervision. |
CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels (2026.acl-long)
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Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu, Jingwen Leng, Yun Lin, Shixuan Sun, Minyi Guo, Jin Song Dong
| Challenge: | Existing approaches to support diverse attention variants trade performance for flexibility . expert-written kernels achieve high efficiency but are difficult to adapt . |
| Approach: | They propose a framework that adapts expert-written attention kernels to GPUs . they use a structured lift–transfer–lower workflow to make execution explicit . |
| Outcome: | The proposed framework outperforms existing frameworks and compilers on diverse variants and GPU platforms. |