A Japanese Masked Language Model for Academic Domain
Hiroki Yamauchi | Tomoyuki Kajiwara | Marie Katsurai | Ikki Ohmukai | Takashi Ninomiya
Proceedings of the Third Workshop on Scholarly Document Processing
We release a pretrained Japanese masked language model for an academic domain. Pretrained masked language models have recently improved the performance of various natural language processing applications. In domains such as medical and academic, which include a lot of technical terms, domain-specific pretraining is effective. While domain-specific masked language models for medical and SNS domains are widely used in Japanese, along with domain-independent ones, pretrained models specific to the academic domain are not publicly available. In this study, we pretrained a RoBERTa-based Japanese masked language model on paper abstracts from the academic database CiNii Articles. Experimental results on Japanese text classification in the academic domain revealed the effectiveness of the proposed model over existing pretrained models.