Xiuying Chen
Assistant Professor of Natural Language Processing
Research interests
Chen has a particular interest in the realm of trustworthy text generation, a critical area that demands the seamless integration of reliability at every stage—from the initial concept and content creation to the final output and reader engagement. Within this overarching theme, she specializes in issues related to robustness, faithfulness, and explainability. She is also interested in text generation for high-stakes domain applications, particularly in the scientific, medical and social fields.
Email
Prior to joining MBZUAI, Chen completed her Ph.D. (2024) at KAUST in the Kingdom of Saudi Arabia. She has relatively rich industrial experience obtained through research internships, including at the Inception Institute of Artificial Intelligence (2019), Xiaomi (2020), Microsoft (2022), and multiple collaborations with NIH, Alibaba, Ant Group, and Tencent.
She received several awards during her studies, including the National Scholarship three times, the Hornbill Elite Programme, the Dean's List Award, the Scientific Research Award, and the SIGIR Travel Grant. Xiuying regularly serves as an AE or program committee member for leading NLP journals and conferences, including ACL, EMNLP, SIGIR, WWW, ICML, and NeurIPS.
- Ph.D. in computer science, King Abdullah University of Science and Technology (KAUST), Kingdom of Saudi Arabia.
- M.Sc. in data science, Peking University, China.
- Bachelor’s degree in information security, Wuhan University, China.
- CEMSE Dean's List Award, KAUST, 2023
- Hornbill Elite Programme, 2022
- SIGIR Student Travel Grant, 2022
- Outstanding Graduate, Beijing, 2021
- Scientific Research Award, Peking University, 2020
- National Scholarship, Peking University, 2019-2020
Chen has published more than 40 peer-reviewed papers at top-tier conferences, in topics related to pre-trained language models, text generation, and recommendation systems.
- Chen X, Liu Y, Zhang X, et al.: "From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News." IJCAI, 2024.
- Chen X, Li M, Gao S, et al.: "A topic-aware summarization framework with different modal side information." SIGIR. 2023.
- Chen X, Long G, Tao C, et al.: "Improving the robustness of summarization systems with dual augmentation." ACL, 2023.
- Chen X, Alamro H, Li M, et al.: "Target-aware abstractive related work generation with contrastive learning." SIGIR 2022.
- Chen X, Li M, Gao X, et al.: "Towards improving faithfulness in abstractive summarization." NeurIPS, 2022.
- Chen X, Alamro H, Li M, et al.: "Capturing relations between scientific papers: An abstractive model for related work section generation." ACL, 2021.