Papers by Minghui Yang
VoxpopuliTTS: a large-scale multilingual TTS corpus for zero-shot speech generation (2025.coling-main)
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Wenrui Liu, Jionghao Bai, Xize Cheng, Jialong Zuo, Ziyue Jiang, Shengpeng Ji, Minghui Fang, Xiaoda Yang, Qian Yang, Zhou Zhao
| Challenge: | Existing multilingual TTS datasets are limited in speech generation fields due to lack of quality data. |
| Approach: | They propose to use 30,000 hours of high-quality speech data across 3 languages . they filter out low-quality text-text pairs and concatenate short transcripts . |
| Outcome: | The proposed dataset comprises 30,000 hours of high-quality speech data, across 3 languages with multiple speakers and styles, suitable for various speech tasks such as TTS and ASR. |
A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)
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Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu
| Challenge: | a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes . |
| Approach: | They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. |
| Outcome: | The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure. |
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)
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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
| Challenge: | Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality. |
| Approach: | They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. |
| Outcome: | The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks. |
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)
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| Challenge: | MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions. |
| Approach: | They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously. |
| Outcome: | The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations. |
Towards Unified Prompt Tuning for Few-shot Text Classification (2022.findings-emnlp)
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Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao
| Challenge: | Prompt-based fine-tuning has boosted performance of Pre-trained Language Models (PLMs) on few-shot text classification, but PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few- shot learning performance on downstream tasks. |
| Approach: | They propose a framework for prompt-based fine-tuning that captures prompting semantics from non-target NLP datasets and propose 'Prompt-Options-Verbalizer' for joint prompt learning across different NLP tasks. |
| Outcome: | Experiments show that the proposed framework outperforms state-of-the-art prompt-based fine-tuning frameworks on few-shot text classification tasks. |
AudioVSR: Enhancing Video Speech Recognition with Audio Data (2024.emnlp-main)
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Xiaoda Yang, Xize Cheng, Jiaqi Duan, Hongshun Qiu, Minjie Hong, Minghui Fang, Shengpeng Ji, Jialong Zuo, Zhiqing Hong, Zhimeng Zhang, Tao Jin
| Challenge: | Recent work has shown poor performance with non-Indo-European languages . previous work primarily utilizes video information to build VSR models . |
| Approach: | They propose a generative model for data inflation that integrates synthetic data with authentic visual data to enhance the VSR model. |
| Outcome: | The proposed model improves on the audio-visual alignment problem in audio-video tasks. |
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)
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| Challenge: | Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking. |
| Approach: | They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result. |
| Outcome: | The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking. |
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)
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| Challenge: | Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems. |
| Approach: | They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance. |
| Outcome: | The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist. |
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs (2025.findings-emnlp)
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| Challenge: | Gradient-based data influence approximation is not feasible in practice. |
| Approach: | They propose a gradient-based data selection framework with clustering and a modified Upper Confidence Bound algorithm to solve this problem. |
| Outcome: | The proposed framework can achieve comparable results to the original gradient-based data selection methods while reducing computational consumption. |
Auto Search Indexer for End-to-End Document Retrieval (2023.findings-emnlp)
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| Challenge: | Generative retrieval heavily relies on the “preprocessed” document identifiers, thus limiting its retrieval performance and ability to retrieve new documents. |
| Approach: | They propose a fully end-to-end retrieval paradigm that can learn the best docids for existing and new documents automatically via a semantic indexing module. |
| Outcome: | The proposed model outperforms baselines on public and industrial datasets and can handle new documents. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)
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Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao
| Challenge: | Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody. |
| Approach: | They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content. |
| Outcome: | The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm. |
Streaming Hallucination Detection in Long Chain-of-Thought Reasoning (2026.findings-acl)
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Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu
| Challenge: | Long chain-of-thought reasoning improves performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. |
| Approach: | They propose to treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level signal that tracks the global evolution of the reasoning state over the entire trajectory. |
| Outcome: | The proposed method enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence. |
SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling (2022.naacl-main)
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| Challenge: | Existing studies on automatic summary evaluation metrics focus on lexical similarity and require a reference summary which is expensive to obtain. |
| Approach: | They propose to use a weakly supervised summary evaluation approach without the presence of reference summaries to transform existing summarization datasets into corrupted reference summarizers. |
| Outcome: | The proposed method outperforms baselines and shows that it improves linguistic quality over all metrics. |
HighMATH: Evaluating Math Reasoning of Large Language Models in Breadth and Depth (2025.findings-emnlp)
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Yan Liu, Minghui Zhang, Bojian Xiong, Yifan Xiao, Yinong Sun, Yating Mei, Longyu Zeng, Jingchao Yang, Yang Wang, Deyi Xiong
| Challenge: | a gap in math models' accuracy has been widened with the development of large language models (LLMs) . a new study aims to bridge this gap by evaluating a set of high-level math reasoning models . |
| Approach: | They propose to evaluate large language models on existing math benchmarks to bridge this gap . they collect 5,293 problems from Chinese senior high school mathematics exams . |
| Outcome: | The proposed model is based on o1-like models and a high-level model. |
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining (2021.acl-long)
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| Challenge: | Existing knowledge-based PLMs are based on linked-entity information, but they only use linked-enemy information as auxiliary information. |
| Approach: | They propose to integrate semantic knowledge from neighbours of linked-entity into a medical PLM that integrates heterogeneous-entities into the homogeneously neighbouring entity structure. |
| Outcome: | Experiments show that SMedBERT outperforms baselines in knowledge-intensive Chinese medical tasks. |
DocAsRef: An Empirical Study on Repurposing Reference-based Summary Quality Metrics as Reference-free Metrics (2023.findings-emnlp)
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| Challenge: | Existing reference-based metrics are limited by their reliance on human input. |
| Approach: | They propose to adapt some reference-based metrics to assess system summary against human-written references. |
| Outcome: | The proposed model outperforms reference-based metrics on two datasets and is comparable to reference-free metrics. |
Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)
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Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang
| Challenge: | Modern NLP workflows require different models for generation and embedding tasks. |
| Approach: | They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder. |
| Outcome: | The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps. |
Meta Distant Transfer Learning for Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Notable PLMs are available for text classification tasks, but performance of PLM on downstream tasks may be limited by the availability of training set. |
| Approach: | They propose a meta-learning framework to learn the transferable knowledge across tasks using PLMs. |
| Outcome: | The proposed framework outperforms baselines on seven datasets and is task-agnostic and unbiased. |
More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (2021.emnlp-main)
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| Challenge: | Existing knowledge-enhanced methods use a single-source homogeneous knowledge base with limited knowledge coverage. |
| Approach: | They propose a multi-source heterogeneous knowledge-enhanced dialogue generation model that leverages multiple knowledge sources to improve knowledge coverage. |
| Outcome: | The proposed model outperforms existing knowledge-enhanced models on a Chinese dataset and shows that it can leverage multiple heterogeneous knowledge sources to improve knowledge coverage. |