# Integrating question answering and text-to-SQL in Portuguese

arXiv: https://arxiv.org/abs/2202.04048 · HTML: https://mlsanity.com/paper/2202.04048/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Retrospective Reader for Machine Reading Comprehension](https://mlsanity.com/paper/2001.09694/) (2020)
- [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019)
- [Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training](https://mlsanity.com/paper/2012.10309/) (2020)
- [PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data](https://mlsanity.com/paper/2008.09144/) (2020)
- [Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension](https://mlsanity.com/paper/1911.00202/) (2019)
- [Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering](https://mlsanity.com/paper/2108.02866/) (2021)
- [mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer](https://mlsanity.com/paper/2110.03546/) (2021)
- [DEEPAG'E: Answering Questions in Portuguese about the Brazilian Environment](https://mlsanity.com/paper/2110.10015/) (2021)

## Built on by (most cited first, 2 of 2)

- [Benchmarks for Pir'a 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change](https://mlsanity.com/paper/2309.10945/) (2023)
- [The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory](https://mlsanity.com/paper/2209.07928/) (2022)

## Abstract (arXiv)

Deep learning transformers have drastically improved systems that automatically answer questions in natural language. However, different questions demand different answering techniques; here we propose, build and validate an architecture that integrates different modules to answer two distinct kinds of queries. Our architecture takes a free-form natural language text and classifies it to send it either to a Neural Question Answering Reasoner or a Natural Language parser to SQL. We implemented a complete system for the Portuguese language, using some of the main tools available for the language and translating training and testing datasets. Experiments show that our system selects the appropriate answering method with high accuracy (over 99%), thus validating a modular question answering strategy.