Automated documentation of programming source code and automated code
generation from natural language are challenging tasks of both practical and
scientific interest. Progress in these areas has been limited by the low
availability of parallel corpora of code and natural language descriptions,
which tend to be small and constrained to specific domains.
In this work we introduce a large and diverse parallel corpus of a hundred
thousands Python functions with their documentation strings ("docstrings")
generated by scraping open source repositories on GitHub. We describe baseline
results for the code documentation and code generation tasks obtained by neural
machine translation. We also experiment with data augmentation techniques to
further increase the amount of training data.
We release our datasets and processing scripts in order to stimulate research
in these areas.