A Japanese Masked Language Model for Academic Domain
A Japanese Masked Language Model for Academic Domain
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发表时间：
2022
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通讯作者：
Hiroki Yamauchi;Tomoyuki Kajiwara;Marie Katsurai;Ikki Ohmukai;Takashi Ninomiya
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作者：
Hiroki Yamauchi;Tomoyuki Kajiwara;Marie Katsurai;Ikki Ohmukai;Takashi Ninomiya
我们发布了一个用于学术领域的预训练日语掩蔽语言模型。预训练的掩蔽语言模型最近提高了各种自然语言处理应用程序的性能。在医学和学术等包含大量技术术语的领域，特定领域的预训练是有效的。虽然针对医疗和SNS领域的特定领域掩蔽语言模型在日语中广泛使用，但沿着领域独立模型，针对学术领域的预训练模型尚未公开。在这项研究中，我们在学术数据库CiNii Articles的论文摘要上预训练了一个基于Roberta的日语掩蔽语言模型。在学术领域的日语文本分类实验结果表明，该模型比现有的预训练模型的有效性。
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.