mRAT-SQL+GAP: A Portuguese Text-to-SQL Transformer

Marcelo Archanjo José, Fabio Gagliardi Cozman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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:

Original languageEnglish
Title of host publicationIntelligent Systems - 10th Brazilian Conference, BRACIS 2021, Proceedings, Part 2
EditorsAndré Britto, Karina Valdivia Delgado
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030916985
StatePublished - 2021
Externally publishedYes
Event10th Brazilian Conference on Intelligent Systems, BRACIS 2021 - Virtual, Online
Duration: 29 Nov 20213 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13074 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Brazilian Conference on Intelligent Systems, BRACIS 2021
CityVirtual, Online


  • BART
  • BERTimbau
  • Deep learning
  • NL2SQL
  • Spider dataset


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