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Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest.
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Noam Chomsky. 1956 · 1956
Earlier work this paper cites.
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George A Miller. 2003 · 2003
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Pengcheng Yin and Graham Neubig. 2017 · 2017
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Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr, and Sebastian Riedel. 2016 · 2016
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