Tree-to-tree Neural Networks for Program Translation

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to consider employing neural network approaches toward tackling this problem. We observe that program translation is a modular procedure, in which one sub-tree in the source is translated into the corresponding target sub-tree at each step. To capture this intuition, we design a tree-to-tree neural network as an encoder-decoder architecture to translate a source tree into a target one. Meanwhile, we develop an attention mechanism for the tree-to-tree model, so that when the decoder expands one non-terminal in the target tree, the attention mechanism locates the corresponding sub-tree in the source to guide the expansion of the decoder. We develop two tasks to evaluate the program translation capability of our tree-to-tree model against two state-of-the-art approaches. We observe that our approach is consistently better than the baselines on all tasks and all metrics with a margin of up to 10%. These results demonstrate that the tree-to-tree model is a promising tool for tackling the program translation problem.

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