Jihun
Choi

Profile

Jihun’s research interest lies in natural language processing and machine learning. He received his Ph.D. and B.S. degree in 2020 and 2014 from the Department of Computer Science, Seoul National University. During his Ph.D. study, his major interest was encoding sentences by extracting their semantics and syntax automatically via deep neural network.

Message

“At Sony AI, I am trying to devote myself to apply natural language processing techniques for achieving the goal of our organization: widening imagination and creativity with AI. I am honored that I can serve on the gastronomy flagship project, the area that is driven by a sophisticated level of creativity, using my knowledge. As a member of Sony AI, I want to reach an unexplored territory of AI research.”

Publications

Literature-based Hypothesis Generation: Predicting the evolution of scientific literature to support scientists

AI4X, 2025
Tarek R Besold, Uchenna Akujuobi, Samy Badreddine, Jihun Choi, Hatem ElShazly, Frederick Gifford, Chrysa Iliopoulou, Kana Maruyama, Kae Nagano, Pablo Sanchez Martin, Thiviyan Thanapalasingam, Alessandra Toniato, Christoph Wehner

Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year [1]. On the one hand, this poses anincreasingly pressing challenge: Effectively navigating this ever-growing body o…

Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach

AIR, 2024
Uchenna Akujuobi, Priyadarshini Kumari, Jihun Choi, Samy Badreddine, Kana Maruyama, Sucheendra K Palaniappan*, Tarek R Besold

Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously imp…

Analysis of Multi-Source Language Training in Cross-Lingual Transfer

ACL, 2024
Seong Hoon Lim*, Taejun Yun*, Jinhyeon Kim*, Jihun Choi, Taeuk Kim

The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition. While cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem, …

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