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 |
