Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker

• URL: http://arxiv.org/abs/2002.00557v2
• Date: Tue, 3 Nov 2020 22:22:57 GMT
• Title: Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker
• Authors: Amol Kelkar, Rohan Relan, Vaishali Bhardwaj, Saurabh Vaichal, Chandra Khatri, Peter Relan
• Abstract summary: We propose a novel discnative re-ranker to improve the performance of generative text-to-rimi models. We analyze relative strengths of the text-to-rimi and re-ranker models for optimal performance. We demonstrate the effectiveness of the re-ranker by applying it to two state-of-the-art text-to-rimi models.
• Score: 1.049360126069332
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To access data stored in relational databases, users need to understand the database schema and write a query using a query language such as SQL. To simplify this task, text-to-SQL models attempt to translate a user's natural language question to corresponding SQL query. Recently, several generative text-to-SQL models have been developed. We propose a novel discriminative re-ranker to improve the performance of generative text-to-SQL models by extracting the best SQL query from the beam output predicted by the text-to-SQL generator, resulting in improved performance in the cases where the best query was in the candidate list, but not at the top of the list. We build the re-ranker as a schema agnostic BERT fine-tuned classifier. We analyze relative strengths of the text-to-SQL and re-ranker models across different query hardness levels, and suggest how to combine the two models for optimal performance. We demonstrate the effectiveness of the re-ranker by applying it to two state-of-the-art text-to-SQL models, and achieve top 4 score on the Spider leaderboard at the time of writing this article.

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