mlsanityData to 7 Oct 2026

AraSpider: Democratizing Arabic-to-SQL

arXiv 2402.07448 ↗First posted 12 Feb 2024cs.CL cs.AI cs.DBcited by 0

Not on the map. Why?

Abstract (arXiv)

This study presents AraSpider, the first Arabic version of the Spider dataset, aimed at improving natural language processing (NLP) in the Arabic-speaking community. Four multilingual translation models were tested for their effectiveness in translating English to Arabic. Additionally, two models were assessed for their ability to generate SQL queries from Arabic text. The results showed that using back translation significantly improved the performance of both ChatGPT 3.5 and SQLCoder models, which are considered top performers on the Spider dataset. Notably, ChatGPT 3.5 demonstrated high-quality translation, while SQLCoder excelled in text-to-SQL tasks. The study underscores the importance of incorporating contextual schema and employing back translation strategies to enhance model performance in Arabic NLP tasks. Moreover, the provision of detailed methodologies for reproducibility and translation of the dataset into other languages highlights the research's commitment to promoting transparency and collaborative knowledge sharing in the field. Overall, these contributions advance NLP research, empower Arabic-speaking researchers, and enrich the global discourse on language comprehension and database interrogation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

14 of 25 references.

References · 14

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  3. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  4. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  5. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  6. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  7. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 123
  8. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
  9. 2020A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamesecited by 36
  10. 2019A Pilot Study for Chinese SQL Semantic Parsingcited by 33
  11. 2020Hybrid Ranking Network for Text-to-SQLcited by 33
  12. 2022MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsingcited by 22
2 more
  1. 2021mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformercited by 15
  2. 2023A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attentioncited by 3

Built on by

Not cited yet.