Program Translation via Code Distillation
Yufan Huang Mengnan Qi Yongqiang Yao Maoquan Wang Bin Gu Colin Clement Neel Sundaresan
Software version migration and program translation are an important and
costly part of the lifecycle of large codebases. Traditional machine
translation relies on parallel corpora for supervised translation, which is not
feasible for program translation due to a dearth of aligned data. Recent
unsupervised neural machine translation techniques have overcome data
limitations by included techniques such as back translation and low level
compiler intermediate representations (IR). These methods face significant
challenges due to the noise in code snippet alignment and the diversity of IRs
respectively. In this paper we propose a novel model called Code Distillation
(CoDist) whereby we capture the semantic and structural equivalence of code in
a language agnostic intermediate representation. Distilled code serves as a
translation pivot for any programming language, leading by construction to
parallel corpora which scale to all available source code by simply applying
the distillation compiler. We demonstrate that our approach achieves
state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks
translation benchmarks, with an average absolute increase of 12.7% on the
TransCoder GeeksforGeeks translation benchmark compare to TransCoder-ST.