@inproceedings{hayati-etal-2018-retrieval,
title = "Retrieval-Based Neural Code Generation",
author = "Hayati, Shirley Anugrah and
Olivier, Raphael and
Avvaru, Pravalika and
Yin, Pengcheng and
Tomasic, Anthony and
Neubig, Graham",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1111/",
doi = "10.18653/v1/D18-1111",
pages = "925--930",
abstract = "In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU."
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<abstract>In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.</abstract>
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%0 Conference Proceedings
%T Retrieval-Based Neural Code Generation
%A Hayati, Shirley Anugrah
%A Olivier, Raphael
%A Avvaru, Pravalika
%A Yin, Pengcheng
%A Tomasic, Anthony
%A Neubig, Graham
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F hayati-etal-2018-retrieval
%X In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.
%R 10.18653/v1/D18-1111
%U https://aclanthology.org/D18-1111/
%U https://doi.org/10.18653/v1/D18-1111
%P 925-930
Markdown (Informal)
[Retrieval-Based Neural Code Generation](https://aclanthology.org/D18-1111/) (Hayati et al., EMNLP 2018)
ACL
- Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, and Graham Neubig. 2018. Retrieval-Based Neural Code Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 925–930, Brussels, Belgium. Association for Computational Linguistics.