@inproceedings{popov-etal-2021-time,
title = "Time-Efficient Code Completion Model for the {R} Programming Language",
author = "Popov, Artem and
Orekhov, Dmitrii and
Litvinov, Denis and
Korolev, Nikolay and
Morgachev, Gleb",
editor = "Lachmy, Royi and
Yao, Ziyu and
Durrett, Greg and
Gligoric, Milos and
Li, Junyi Jessy and
Mooney, Ray and
Neubig, Graham and
Su, Yu and
Sun, Huan and
Tsarfaty, Reut",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.4",
doi = "10.18653/v1/2021.nlp4prog-1.4",
pages = "34--39",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Time-Efficient Code Completion Model for the R Programming Language
%A Popov, Artem
%A Orekhov, Dmitrii
%A Litvinov, Denis
%A Korolev, Nikolay
%A Morgachev, Gleb
%Y Lachmy, Royi
%Y Yao, Ziyu
%Y Durrett, Greg
%Y Gligoric, Milos
%Y Li, Junyi Jessy
%Y Mooney, Ray
%Y Neubig, Graham
%Y Su, Yu
%Y Sun, Huan
%Y Tsarfaty, Reut
%S Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F popov-etal-2021-time
%X 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.
%R 10.18653/v1/2021.nlp4prog-1.4
%U https://aclanthology.org/2021.nlp4prog-1.4
%U https://doi.org/10.18653/v1/2021.nlp4prog-1.4
%P 34-39
Markdown (Informal)
[Time-Efficient Code Completion Model for the R Programming Language](https://aclanthology.org/2021.nlp4prog-1.4) (Popov et al., NLP4Prog 2021)
ACL