Google Research

Deep Learning for Classical Japanese Literature

  • Alex Lamb
  • Asanobu Kitamoto
  • David Ha
  • Kazuaki Yamamoto
  • Mikel Bober-Irizar
  • Tarin Clanuwat
Neural Information Processing Systems Machine Learning for Creativity and Design Workshop (2018)

Abstract

Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Dataset available at this url.

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