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

• URL: http://arxiv.org/abs/2205.11116v1
• Date: Mon, 23 May 2022 08:20:41 GMT
• Title: Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
• Authors: Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
• Abstract summary: Back-translation is widely known for its effectiveness for neural machine translation when little to no parallel data is available. We propose performing back-translation via code summarization and generation. We show that our proposed approach performs competitively with state-of-the-art methods.
• Score: 86.08359401867577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Back-translation is widely known for its effectiveness for neural machine translation when little to no parallel data is available. 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, it is compelling to train them to build programming language translation systems via back-translation. 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 target-to-NL-to-source generation. We show that our proposed approach performs competitively with state-of-the-art methods.

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