A Method for Building a Commonsense Inference Dataset based on Basic Events

no code implementations • EMNLP 2020 • Kazumasa Omura, Daisuke Kawahara, Sadao Kurohashi

We present a scalable, low-bias, and low-cost method for building a commonsense inference dataset that combines automatic extraction from a corpus and crowdsourcing.

Multiple-choice Transfer Learning

Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks

no code implementations • COLING 2022 • Kazumasa Omura, Sadao Kurohashi

Contingent reasoning is one of the essential abilities in natural language understanding, and many language resources annotated with contingent relations have been constructed.

Natural Language Understanding Relation +1

A System for Worldwide COVID-19 Information Aggregation

no code implementations • EMNLP (NLP-COVID19) 2020 • Akiko Aizawa, Frederic Bergeron, Junjie Chen, Fei Cheng, Katsuhiko Hayashi, Kentaro Inui, Hiroyoshi Ito, Daisuke Kawahara, Masaru Kitsuregawa, Hirokazu Kiyomaru, Masaki Kobayashi, Takashi Kodama, Sadao Kurohashi, Qianying Liu, Masaki Matsubara, Yusuke Miyao, Atsuyuki Morishima, Yugo Murawaki, Kazumasa Omura, Haiyue Song, Eiichiro Sumita, Shinji Suzuki, Ribeka Tanaka, Yu Tanaka, Masashi Toyoda, Nobuhiro Ueda, Honai Ueoka, Masao Utiyama, Ying Zhong

The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.

Machine Translation Translation

Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction

no code implementations • WS 2019 • Hirokazu Kiyomaru, Kazumasa Omura, Yugo Murawaki, Daisuke Kawahara, Sadao Kurohashi

Typical event sequences are an important class of commonsense knowledge.