TL;DR
This paper presents a modular system for Portuguese that integrates question answering and text-to-SQL techniques, accurately classifying queries to improve natural language understanding and database interaction.
Contribution
It introduces a novel architecture that combines question answering and SQL parsing modules for Portuguese, validated with high accuracy.
Findings
System achieves over 99% accuracy in query classification
Effective integration of QA and text-to-SQL modules for Portuguese
Validates modular question answering strategy
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.
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