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            <namePart type="family">Vlachos</namePart>
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    <abstract>Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previous research primarily explored code pre-training and expanded it through multi-modality and multi-tasking, yet the data for downstream tasks remain modest in size. Focusing on data utilization for downstream tasks, we propose and adapt augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. Further analysis suggests that our methods work orthogonally and show benefits in output code style and numeric consistency. We also discuss test data imperfections.</abstract>
    <identifier type="citekey">chen-lampouras-2023-exploring</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-eacl.114</identifier>
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%0 Conference Proceedings
%T Exploring Data Augmentation for Code Generation Tasks
%A Chen, Pinzhen
%A Lampouras, Gerasimos
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F chen-lampouras-2023-exploring
%X Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previous research primarily explored code pre-training and expanded it through multi-modality and multi-tasking, yet the data for downstream tasks remain modest in size. Focusing on data utilization for downstream tasks, we propose and adapt augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. Further analysis suggests that our methods work orthogonally and show benefits in output code style and numeric consistency. We also discuss test data imperfections.
%R 10.18653/v1/2023.findings-eacl.114
%U https://aclanthology.org/2023.findings-eacl.114
%U https://doi.org/10.18653/v1/2023.findings-eacl.114
%P 1542-1550

Markdown (Informal)

[Exploring Data Augmentation for Code Generation Tasks](https://aclanthology.org/2023.findings-eacl.114) (Chen & Lampouras, Findings 2023)

• Exploring Data Augmentation for Code Generation Tasks (Chen & Lampouras, Findings 2023)

ACL

• Pinzhen Chen and Gerasimos Lampouras. 2023. Exploring Data Augmentation for Code Generation Tasks. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1542–1550, Dubrovnik, Croatia. Association for Computational Linguistics.