Yao, Kaichun (姚开春)
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Assistant Professor, Postdoctor |
About me
Dr. Kaichun Yao is currently an Assistant Professor and works as a Postdoctor in Institute of Software, Chinese Academy of Sciences. He was previously the Research Scientist at Baidu Inc from 2019 to 2023. He received the B.S. degree and the M.S. degree from the Southwest Jiaotong University in 2012 and in 2016, and the PhD degree from the Universtiy of Chinese Academy of Sciences in 2019. His general research interests are natural language processing and data mining, with a focus on knowledge discovery and talent computing.
Research
My research interests include:
NLP including Information Extraction, Text Summarization and Dialogue Generation
Knowledge Graph, GNN and Reinforcement Learning
Data Mining including Talent Computing and Recommendation Algorithm
Multi-modal Representation Learning between Vision and Language
Selected publications
Conference publications
Chuyu Fang, Chuan Qin, Qi Zhang, Kaichun Yao, Jingshuai Zhang, Hengshu Zhu*, Fuzhen Zhuang*, Hui Xiong, "RecruitPro: A Pretrained Language Model with Skill-Aware Prompt Learning for Intelligent Recruitment", In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2023), 2023
Chenguang Du, Kaichun Yao, Hengshu Zhu*, Deqing Wang*, Fuzhen Zhuang, and Hui Xiong, "Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph", In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2023), 2023
Kaichun Yao, Jingshuai Zhang, Chuan Qin*, Xin Song, Peng Wang, Hengshu Zhu*, and Hui Xiong, "ResuFormer: Semantic Structure Understanding for Resumes via Multi-Modal Pre-training", In Proceedings of the 39th IEEE International Conference on Data Engineering (ICDE-2023), 2023
Kaichun Yao, Jingshuai Zhang, Chuan Qin, Peng Wang, Hengshu Zhu*, Hui Xiong, "Knowledge Enhanced Person-Job Fit for Talent Recruitment", In Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE-2022), 2022[pdf]
Kaichun Yao, Chuan Qin, Hengshu Zhu*, Chao Ma, Jingshuai Zhang, Yi Du, Hui Xiong, "An Interactive Neural Network Approach to Keyphrase Extraction in Talent Recruitment", In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM-2021), 2021[pdf]
Kaichun Yao, Libo Zhang*, Tiejian Luo, Lili Tao, Yanjun Wu, "Teaching Machines to Ask Questions", IJCAI 2018: 4546-4552[pdf]
Journal publications
Kaichun Yao, Hao Wang, Chuan Qin, Hengshu Zhu, Yanjun Wu, Libo Zhang, "CARL: Unsupervised Code-Based Adversarial Attacks for Programming Language Models via Reinforcement Learning", In ACM Transactions on Software Engineering and Methodology (ACM TOSEM), 2024[pdf]
Chenguang Du, Kaichun Yao*, Hengshu Zhu, Deqing Wang, Fuzhen Zhuang, Hui Xiong, "Mining technology trends in scientific publications: a graph propagated neural topic modeling approach", Knowledge and Information Systems. 66(5): 3085-3114 (2024)[pdf]
Chuan Qin, Hengshu Zhu, Dazhong Shen, Ying Sun, Kaichun Yao, Peng Wang, Hui Xiong*, "Automatic Skill-oriented Question Generation and Recommendation for Intelligent Job Interviews", In ACM Transactions on Information Systems (ACM TOIS), 2023[pdf]
Chuan Qin, Kaichun Yao, Hengshu Zhu*, Tong Xu, Dazhong Shen, Enhong Chen, Hui Xiong*, "Towards Automatic Job Description Generation with Capability-Aware Neural Networks", In IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2022[pdf]
Kaichun Yao, Libo Zhang*, Tiejian Luo, Dawei Du, Yanjun Wu, "Non-deterministic and emotional chatting machine: learning emotional conversation generation using conditional variational autoencoders", Neural Comput. Appl. 33(11): 5581-5589 (2021)[pdf]
Kaichun Yao, Libo Zhang*, Dawei Du, Tiejian Luo, Lili Tao, Yanjun Wu, "Dual Encoding for Abstractive Text Summarization", IEEE Trans. Cybern. 50(3): 985-996 (2020)[pdf]
Kaichun Yao, Libo Zhang*, Tiejian Luo, Yanjun Wu, "Deep reinforcement learning for extractive document summarization", Neurocomputing 284: 52-62 (2018)[pdf]
Note: * indicates the corresponding author.