  Code search is a task to find programming codes that semantically match the
given natural language queries. Even though some of the existing datasets for
this task are multilingual on the programming language side, their query data
are only in English. In this research, we create a multilingual code search
dataset in four natural and four programming languages using a neural machine
translation model. Using our dataset, we pre-train and fine-tune the
Transformer-based models and then evaluate them on multiple code search test
sets. Our results show that the model pre-trained with all natural and
programming language data has performed best in most cases. By applying
back-translation data filtering to our dataset, we demonstrate that the
translation quality affects the model's performance to a certain extent, but
the data size matters more.
