A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attention

• URL: http://arxiv.org/abs/2306.14256v1
• Date: Sun, 25 Jun 2023 14:28:12 GMT
• Title: A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attention
• Authors: Marcelo Archanjo Jose and Fabio Gagliardi Cozman
• Abstract summary: We present techniques that allow long text sequences to be handled by transformers with up to 512 input tokens. In addition, we used a multilingual approach with the mT5-large model fine-tuned with a data-augmented Spider dataset in four languages simultaneously.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long sequences of text are challenging in the context of transformers, due to quadratic memory increase in the self-attention mechanism. As this issue directly affects the translation from natural language to SQL queries (as techniques usually take as input a concatenated text with the question and the database schema), we present techniques that allow long text sequences to be handled by transformers with up to 512 input tokens. We propose a training process with database schema pruning (removal of tables and columns names that are useless for the query of interest). In addition, we used a multilingual approach with the mT5-large model fine-tuned with a data-augmented Spider dataset in four languages simultaneously: English, Portuguese, Spanish, and French. Our proposed technique used the Spider dataset and increased the exact set match accuracy results from 0.718 to 0.736 in a validation dataset (Dev). Source code, evaluations, and checkpoints are available at: \underline{https://github.com/C4AI/gap-text2sql}.

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