Deep Learning for Classical Japanese Literature
This provides a new benchmark for researchers interested in culturally significant problems, though it is incremental as it adapts existing dataset formats to a new domain.
The authors tackled the lack of culturally relevant benchmark tasks in machine learning by introducing Kuzushiji-MNIST and other datasets for classical Japanese literature, aiming to engage the community in this domain.
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 https://github.com/rois-codh/kmnist