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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