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

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Toronto Deep Learning Series, 30 July 2018

For slides and more information, visit https://tdls.a-i.science/events/2018-07-30/

Paper Review: https://arxiv.org/abs/1807.01784

Speaker: Alex Hesammohseni
Organizer: https://www.linkedin.com/in/amirfz/

Host: Microsoft Canada

Paper abstract:
"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."



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Tags:
deep learning
programming language translation
tree lstm
neural code parsing