no code implementations • LANTERN (COLING) 2020 • Diana Galvan-Sosa, Jun Suzuki, Kyosuke Nishida, Koji Matsuda, Kentaro Inui
Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems.
no code implementations • COLING 2022 • Satoshi Sekine, Kouta Nakayama, Masako Nomoto, Maya Ando, Asuka Sumida, Koji Matsuda
The training data were provided by Japanese categorization and the language links, and the task was to categorize the Wikipedia pages into 30 languages, with no language links from Japanese Wikipedia (20M pages in total).
1 code implementation • 27 Jun 2022 • Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.
no code implementations • 15 Oct 2021 • Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka
First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance.
no code implementations • AKBC 2021 • Satoshi Sekine, Kouta Nakayama, Maya Ando, Yu Usami, Masako Nomoto, Koji Matsuda
In our "Resource by Collaborative Contribution (RbCC)" scheme, we conducted a shared task of structuring Wikipedia to attract participants but simultaneously submitted results are used to construct a knowledge base.
no code implementations • WS 2018 • Diana Galvan, Naoaki Okazaki, Koji Matsuda, Kentaro Inui
Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain.