Summary of Ezsql: An Sql Intermediate Representation For Improving Sql-to-text Generation, by Meher Bhardwaj et al.
EzSQL: An SQL intermediate representation for improving SQL-to-text Generation
by Meher Bhardwaj, Hrishikesh Ethari, Dennis Singh Moirangthem
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel approach to the SQL-to-text generation task, which typically employs template-based, Seq2Seq, tree-to-sequence, and graph-to-sequence models. Recent advancements leverage pre-trained generative language models within the Seq2Seq framework. However, treating SQL as a sequence of inputs to these models is not optimal. The paper presents EzSQL, a new intermediate representation that aligns SQL with natural language text sequences by simplifying queries, modifying operators and keywords, and removing set operators. The proposed model uses EzSQL as input to a pre-trained generative language model for generating text descriptions. Evaluations on WikiSQL and Spider datasets demonstrate the model’s effectiveness as a state-of-the-art method. Additionally, generating pre-training data using this model enhances the performance of Text-to-SQL parsers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how computers can understand SQL (a programming language) better by changing it into something more like natural human language. Right now, computers use different models to convert SQL into text, but these models aren’t perfect. The researchers suggest a new way of representing SQL that makes it easier for computers to understand and generate text descriptions from SQL queries. They test their idea on two datasets (WikiSQL and Spider) and show that it works really well. This could help make computers better at understanding and generating human language, which is important for tasks like asking questions or summarizing data. |
Keywords
» Artificial intelligence » Language model » Seq2seq » Text generation
