  The translation of natural language questions to SQL queries has attracted
growing attention, in particular in connection with transformers and similar
language models. A large number of techniques are geared towards the English
language; in this work, we thus investigated translation to SQL when input
questions are given in the Portuguese language. To do so, we properly adapted
state-of-the-art tools and resources. We changed the RAT-SQL+GAP system by
relying on a multilingual BART model (we report tests with other language
models), and we produced a translated version of the Spider dataset. Our
experiments expose interesting phenomena that arise when non-English languages
are targeted; in particular, it is better to train with original and translated
training datasets together, even if a single target language is desired. This
multilingual BART model fine-tuned with a double-size training dataset (English
and Portuguese) achieved 83% of the baseline, making inferences for the
Portuguese test dataset. This investigation can help other researchers to
produce results in Machine Learning in a language different from English. Our
multilingual ready version of RAT-SQL+GAP and the data are available,
open-sourced as mRAT-SQL+GAP at: https://github.com/C4AI/gap-text2sql
