| Full text | |
| Author(s): Show less - |
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
Total Authors: 12
|
| Document type: | Journal article |
| Source: | COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE, PROPOR 2022; v. 13208, p. 10-pg., 2022-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |