@inproceedings{sekizawa-etal-2023-constructing,
    title = "Constructing Multilingual Code Search Dataset Using Neural Machine Translation",
    author = "Sekizawa, Ryo  and
      Duan, Nan  and
      Lu, Shuai  and
      Yanaka, Hitomi",
    editor = "Padmakumar, Vishakh  and
      Vallejo, Gisela  and
      Fu, Yao",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-srw.10",
    doi = "10.18653/v1/2023.acl-srw.10",
    pages = "69--75",
    abstract = "Code search is a task to find programming codes that semantically match the given natural language queries. Even though some of the existing datasets for this task are multilingual on the programming language side, their query data are only in English. In this research, we create a multilingual code search dataset in four natural and four programming languages using a neural machine translation model. Using our dataset, we pre-train and fine-tune the Transformer-based models and then evaluate them on multiple code search test sets. Our results show that the model pre-trained with all natural and programming language data has performed best in most cases. By applying back-translation data filtering to our dataset, we demonstrate that the translation quality affects the model{'}s performance to a certain extent, but the data size matters more.",
}

Code search is a task to find programming codes that semantically match the given natural language queries. Even though some of the existing datasets for this task are multilingual on the programming language side, their query data are only in English. In this research, we create a multilingual code search dataset in four natural and four programming languages using a neural machine translation model. Using our dataset, we pre-train and fine-tune the Transformer-based models and then evaluate them on multiple code search test sets. Our results show that the model pre-trained with all natural and programming language data has performed best in most cases. By applying back-translation data filtering to our dataset, we demonstrate that the translation quality affects the model{'}s performance to a certain extent, but the data size matters more.
Sekizawa, Ryo Duan, Nan Lu, Shuai Yanaka, Hitomi
Constructing Multilingual Code Search Dataset Using Neural Machine Translation