Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang · 2023
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.1