Papers by Xiaohu Zhu
CCSRD: Content-Centric Speech Representation Disentanglement Learning for End-to-End Speech Translation (2023.findings-emnlp)
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| Challenge: | Existing speech-to-text translation models can extract features from speech inputs, but they may include non-linguistic speech factors such as pitch, timbre and speaker identity. |
| Approach: | They propose a content-centric speech representation disentanglement learning framework for speech translation that decomposes speech representations into content representations and non-linguistic representations via representation disentanglement learning. |
| Outcome: | The proposed framework outperforms state-of-the-art speech translation models and cascaded models on five translation directions. |
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)
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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation. |
| Approach: | They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved. |
| Outcome: | The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment. |
Learning SQL Like a Human: Structure-Aware Curriculum Learning for Text-to-SQL Generation (2025.findings-emnlp)
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| Challenge: | Existing models struggle with complex queries, especially multi-table joins and reasoning. |
| Approach: | They propose to build a model with synthetic training samples and a structure-aware curriculum learning framework for enhancing SQL generation. |
| Outcome: | The proposed model improves on the existing model on the Spider and Bird benchmarks. |
Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning (2024.findings-acl)
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| Challenge: | Existing CodePre-trained models struggle to generalize due to superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities. |
| Approach: | They propose a framework that integrates multi-task learning with Large Language Models to effectively mine deep-seated vulnerability features. |
| Outcome: | The proposed framework surpasses seven state-of-the-art models in effectiveness, generalization, and robustness. |
Towards a Deep Understanding of Multilingual End-to-End Speech Translation (2023.findings-emnlp)
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| Challenge: | Recent years have witnessed the rapid development of end-to-end speech-totext translation (ST) which has demonstrated remarkable performance and outperformed conventional cascaded systems. |
| Approach: | They employ Singular Value Canonical Correlation Analysis to analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages. |
| Outcome: | The proposed approach outperforms existing cascaded systems in predicting phonetic features and improves translation quality. |
CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation (2023.findings-acl)
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| Challenge: | End-to-end speech-totext translation (ST) data are limited due to the limited resources. |
| Approach: | They propose a knowledge distillation framework for speech translation that integrates knowledge from machine translation and decouples knowledge from non-target class knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a benchmark dataset. |
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment (2026.findings-acl)
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Tianyu Dong, Yangyang Liu, Jiang Zhou, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Shaolin Zhu, Deyi Xiong
| Challenge: | Low-resource language tokens are often routed to different experts than those activated by high-resourced inputs, which hinders their efficacy in multilingual contexts. |
| Approach: | They propose a framework to transfer specialized capabilities from high-resource languages as anchors to low-resourced languages by using a symmetric Jensen-Shannon constraint. |
| Outcome: | The proposed framework outperforms standard instruction tuning on 5 low-resource languages and 3 benchmarks. |