Program Language Translation Using a Grammar-Driven Tree-to-Tree Model

By M. Drissi, Olivia Watkins, Aditya Khant, Vivaswat Ojha, Pedro Sandoval Segura, Rakia Segev, Eric Weiner, Roberto Keller. 2018

18 citations recorded by the source corpus.

The task of translating between programming languages differs from the challenge of translating natural languages in that programming languages are designed with a far more rigid set of structural and grammatical rules. Previous work has used a tree-to-tree encoder/decoder model to take advantage of the inherent tree structure of programs during translation. Neural decoders, however, by default do not exploit known grammar rules of the target language. In this paper, we describe a tree decoder that leverages knowledge of a language's grammar rules to exclusively generate syntactically correct programs. We find that this grammar-based tree-to-tree model outperforms the state of the art tree-to-tree model in translating between two programming languages on a previously used synthetic task.

Subfields: Artificial Intelligence, Information Systems, Code Generation & Program Synthesis

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Citations in the corpus

Unified Pre-training for Program Understanding and Generation (2021, background): “Code Translation requires a model to generate an equivalent code in the target PL from the input code written in the source PL. Note that the source and target PL can be the same.”