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        <title>On the Embeddings of Variables in Recurrent Neural Networks for Source Code</title>
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            <title>Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
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            <namePart type="given">Anna</namePart>
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            <namePart type="given">Yichao</namePart>
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    <abstract>Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An example of this specificity is that the semantics of a variable is defined not only by its name but also by the contexts in which the variable occurs. In this work, we develop dynamic embeddings, a recurrent mechanism that adjusts the learned semantics of the variable when it obtains more information about the variable’s role in the program. We show that using the proposed dynamic embeddings significantly improves the performance of the recurrent neural network, in code completion and bug fixing tasks.</abstract>
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%0 Conference Proceedings
%T On the Embeddings of Variables in Recurrent Neural Networks for Source Code
%A Chirkova, Nadezhda
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F chirkova-2021-embeddings
%X Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An example of this specificity is that the semantics of a variable is defined not only by its name but also by the contexts in which the variable occurs. In this work, we develop dynamic embeddings, a recurrent mechanism that adjusts the learned semantics of the variable when it obtains more information about the variable’s role in the program. We show that using the proposed dynamic embeddings significantly improves the performance of the recurrent neural network, in code completion and bug fixing tasks.
%R 10.18653/v1/2021.naacl-main.213
%U https://aclanthology.org/2021.naacl-main.213
%U https://doi.org/10.18653/v1/2021.naacl-main.213
%P 2679-2689

Markdown (Informal)

[On the Embeddings of Variables in Recurrent Neural Networks for Source Code](https://aclanthology.org/2021.naacl-main.213) (Chirkova, NAACL 2021)

• On the Embeddings of Variables in Recurrent Neural Networks for Source Code (Chirkova, NAACL 2021)

ACL

• Nadezhda Chirkova. 2021. On the Embeddings of Variables in Recurrent Neural Networks for Source Code. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2679–2689, Online. Association for Computational Linguistics.