Integrating question answering and text-to-SQL in Portuguese
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 8 Feb 2022.
Builds on
17 of 27 references.
References · 17
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
- 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
- 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 198
- 2020Retrospective Reader for Machine Reading Comprehensioncited by 86
- 2019Text-to-SQL Generation for Question Answering on Electronic Medical Recordscited by 76
- 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
5 more
- 2020PTT5: Pretraining and validating the T5 model on Brazilian Portuguese datacited by 35
- 2019Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehensioncited by 27
- 2021Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answeringcited by 25
- 2021mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformercited by 15
- 2021DEEPAG'E: Answering Questions in Portuguese about the Brazilian Environmentcited by 5
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.