Tianyu Yang2025-02-17T15:30:28+01:00

Tianyu Yang

Doctoral Researcher

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Research Interest

Tianyu’s current research focuses on the development of multi-modal document processing system for intelligent and automatic data ingestion. Besides, he is also interested in the general generative model and reinforcement learning.

His primary research interest can be summarized as:

  • Natural Language Processing
  • Reinforcement Learning
  • Variational Inference
  • Multi-modal Large Language Models
  • Information Extraction

SHORT CV

12/2024 – Present

Ph.D Student
University of Göttingen, Germany

08/2022 – 07/2024

Doctoral Researcher
Technichal University of Darmstadt, Germany

09/2019 – 06/2022

M.Sc. Computer Science
Civil Aviation University of China, China

09/2015 – 07/2019

B.Sc. Aircraft Manufacturing Engineering
Civil Aviation University of China, China

SELECTED PUBLICATIONS

Robust Utility-Preserving Text Anonymization Based on Large Language Models
Yang, Tianyu, Xiaodan Zhu, and Iryna Gurevych.
arXiv preprint arXiv:2407.11770 (2024).
(PDF)

Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation
Tianyu Yang, Thy Thy Tran, and Iryna Gurevych
In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4718–4735, Singapore. Association for Computational Linguistics.
(PDF)

HTKG: Deep keyphrase generation with neural hierarchical topic guidance
Zhang, Yuxiang, Tao Jiang, Tianyu Yang, Xiaoli Li, and Suge Wang.
In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1044-1054. 2022.
(PDF)

Hyperbolic deep keyphrase generation
Zhang, Yuxiang, Tianyu Yang, Tao Jiang, Xiaoli Li, and Suge Wang
In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 521-536. Cham: Springer International Publishing, 2022.
(PDF)

Boosting KG-to-Text Generation via Multi-granularity Graph Representations
Yang, Tianyu, Yuxiang Zhang, and Tao Jiang
In 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-9. IEEE, 2022.
(PDF)

Order-guided deep neural network for emotion-cause pair prediction
Fan, Wei, Yuexuan Zhu, Ziyun Wei, Tianyu Yang, W. H. Ip, and Yuxiang Zhang
Applied Soft Computing 112 (2021): 107818.
(PDF)

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