Integrating Question Answering and Text-to-SQL in Portuguese

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Jose, Marcos Menon ; Jose, Marcelo Archanjo ; Maua, Denis Deratani ; Cozman, Fabio Gagliardi ; Pinheiro, V ; Gamallo, P ; Amaro, R ; Scarton, C ; Batista, F ; Silva, D ; Magro, C ; Pinto, H

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE, PROPOR 2022; v. 13208, p. 10-pg., 2022-01-01.

Resumo

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. (AU)

• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia