We propose a new neural network architecture and use it for the task of statement-by-
statement alignment of source code and its compiled object code. Our architecture learns
the alignment between the two sequences–one being the translation of the other–by
mapping each statement to a context-dependent representation vector and aligning such
vectors using a grid of the two sequence domains. Our experiments include short C
functions, both artificial and human-written, and show that our neural network architecture is …