Paper ID: 2201.11252

Semantic Code Classification for Automated Machine Learning

Polina Guseva, Anastasia Drozdova, Natalia Denisenko, Daria Sapozhnikova, Ivan Pyaternev, Anna Scherbakova, Andrey Ustuzhanin

A range of applications for automatic machine learning need the generation process to be controllable. In this work, we propose a way to control the output via a sequence of simple actions, that are called semantic code classes. Finally, we present a semantic code classification task and discuss methods for solving this problem on the Natural Language to Machine Learning (NL2ML) dataset.

Submitted: Jan 25, 2022