AraSpider：推动阿拉伯语到SQL的普及
AraSpider: Democratizing Arabic-to-SQL
AI总结：
本研究推出首个阿拉伯语版Spider数据集AraSpider，通过评估多语言翻译与SQL生成模型，证明回译策略显著提升ChatGPT 3.5和SQLCoder性能，推动了阿拉伯语NLP研究。
摘要：
本研究提出了AraSpider，这是Spider数据集的首个阿拉伯语版本，旨在改善阿拉伯语社区的自然语言处理（NLP）。研究测试了四个多语言翻译模型在英译阿任务中的效果。此外，还评估了两个模型从阿拉伯语文本生成SQL查询的能力。结果表明，使用回译显著提升了ChatGPT 3.5和SQLCoder模型的性能，二者被认为是Spider数据集上的最佳模型。值得注意的是，ChatGPT 3.5展现了高质量翻译，而SQLCoder在text-to-SQL任务中表现优异。研究强调了结合上下文模式和采用回译策略以增强阿拉伯语NLP任务模型性能的重要性。此外，提供可复现的详细方法及将数据集翻译为其他语言，突显了本研究对促进该领域透明度与协作知识共享的承诺。总体而言，这些贡献推动了NLP研究，赋能阿拉伯语研究者，并丰富了关于语言理解和数据库查询的全球讨论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
