Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages

Bibliographic Details
Title: Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
Authors: Ahmad, Wasi Uddin, Chakraborty, Saikat, Ray, Baishakhi, Chang, Kai-Wei
Publication Year: 2022
Collection: Computer Science
Subject Terms: Computer Science - Computation and Language, Computer Science - Programming Languages
More Details: Back-translation is widely known for its effectiveness in neural machine translation when there is little to no parallel data. In this approach, a source-to-target model is coupled with a target-to-source model trained in parallel. The target-to-source model generates noisy sources, while the source-to-target model is trained to reconstruct the targets and vice versa. Recent developments of multilingual pre-trained sequence-to-sequence models for programming languages have been very effective for a broad spectrum of downstream software engineering tasks. Hence, training them to build programming language translation systems via back-translation is compelling. However, these models cannot be further trained via back-translation since they learn to output sequences in the same language as the inputs during pre-training. As an alternative, we propose performing back-translation via code summarization and generation. In code summarization, a model learns to generate natural language (NL) summaries given code snippets. In code generation, the model learns to do the opposite. Therefore, target-to-source generation in back-translation can be viewed as a target-to-NL-to-source generation. We show that our proposed approach performs competitively with state-of-the-art methods. We have made the code publicly available.
Comment: Accepted to EACL 2023 (Main)
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2205.11116
Accession Number: edsarx.2205.11116
Database: arXiv
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  Data: Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
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  Data: <searchLink fieldCode="AR" term="%22Ahmad%2C+Wasi+Uddin%22">Ahmad, Wasi Uddin</searchLink><br /><searchLink fieldCode="AR" term="%22Chakraborty%2C+Saikat%22">Chakraborty, Saikat</searchLink><br /><searchLink fieldCode="AR" term="%22Ray%2C+Baishakhi%22">Ray, Baishakhi</searchLink><br /><searchLink fieldCode="AR" term="%22Chang%2C+Kai-Wei%22">Chang, Kai-Wei</searchLink>
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  Data: Back-translation is widely known for its effectiveness in neural machine translation when there is little to no parallel data. In this approach, a source-to-target model is coupled with a target-to-source model trained in parallel. The target-to-source model generates noisy sources, while the source-to-target model is trained to reconstruct the targets and vice versa. Recent developments of multilingual pre-trained sequence-to-sequence models for programming languages have been very effective for a broad spectrum of downstream software engineering tasks. Hence, training them to build programming language translation systems via back-translation is compelling. However, these models cannot be further trained via back-translation since they learn to output sequences in the same language as the inputs during pre-training. As an alternative, we propose performing back-translation via code summarization and generation. In code summarization, a model learns to generate natural language (NL) summaries given code snippets. In code generation, the model learns to do the opposite. Therefore, target-to-source generation in back-translation can be viewed as a target-to-NL-to-source generation. We show that our proposed approach performs competitively with state-of-the-art methods. We have made the code publicly available.<br />Comment: Accepted to EACL 2023 (Main)
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      – SubjectFull: Computer Science - Programming Languages
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      – TitleFull: Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
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            NameFull: Ahmad, Wasi Uddin
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            NameFull: Chakraborty, Saikat
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            NameFull: Chang, Kai-Wei
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