July 11, 2022, 1:10 a.m. | Marc Szafraniec, Baptiste Roziere, Hugh Leather Francois Charton, Patrick Labatut, Gabriel Synnaeve

cs.LG updates on arXiv.org arxiv.org

In this paper, we leverage low-level compiler intermediate representations
(IR) to improve code translation. Traditional transpilers rely on syntactic
information and handcrafted rules, which limits their applicability and
produces unnatural-looking code. Applying neural machine translation (NMT)
approaches to code has successfully broadened the set of programs on which one
can get a natural-looking translation. However, they treat the code as
sequences of text tokens, and still do not differentiate well enough between
similar pieces of code which have different semantics …

arxiv code pl translation

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Principal Applied Scientist

@ Microsoft | Redmond, Washington, United States

Data Analyst / Action Officer

@ OASYS, INC. | OASYS, INC., Pratt Avenue Northwest, Huntsville, AL, United States