Application of noise filter mechanism for t5-based text-to-sql generation

Abstract

The objective of the text-to-SQL task is to convert natural language queries into SQL queries. However, the presence of extensive text-to-SQL datasets across multiple domains, such as Spider, introduces the challenge of effectively generalizing to unseen data. Existing semantic parsing models have struggled to achieve notable performance improvements on these crossdomain datasets. As a result, recent advancements have focused on leveraging pre-trained language models to address this issue and enhance performance in text-to-SQL tasks. These approaches represent the latest and most promising attempts to tackle the challenges associated with generalization and performance improvement in this field. This paper proposes an approach to evaluate and use the Seq2Seq model providing the encoder with the most pertinent schema items as the input and to generate accurate and valid cross-domain SQL queries using the decoder by understanding the skeleton of the target SQL query. The proposed approach is evaluated using Spider dataset which is a well-known dataset for text-to-sql task and able to get promising results where the Exact Match accuracy and Execution accuracy has been boosted to 72.7% and 80.2% respectively compared to other best related approaches.