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Time-Efficient Code Completion Model for the R Programming Languageстатья
- Авторы: Popov Artem, Orekhov Dmitrii, Litvinov Denis, Korolev Nikolay, Morgachev Gleb
- Сборник: Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)
- Год издания: 2021
- Место издания: Association for Computational Linguistics
- Первая страница: 34
- Последняя страница: 39
- DOI: 10.18653/v1/2021.nlp4prog-1.4
- Аннотация: In this paper we present a deep learning code completion model for the R language. We introduce several techniques to utilize language modeling based architecture in the code completion task. With these techniques, the model requires low resources, but still achieves high quality. We also present an evaluation dataset for the R language completion task. Our dataset contains multiple autocompletion usage contexts that provides robust validation results. The dataset is publicly available.
- Добавил в систему: Королев Николай Сергеевич