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.

本研究提出了 AraSpider，这是首个用于改进阿拉伯语社区中自然语言处理（NLP）的 Spider 数据集的阿拉伯语版本。通过测试四种多语言翻译模型的效果来将英语转换为阿拉伯语，并评估两种模型在从阿拉伯文本生成 SQL 查询方面的能力。结果表明，使用回译能显著提高 ChatGPT 3.5 和 SQLCoder 模型的性能，这两个模型在 Spider 数据集中被认为是顶尖的表现者。值得注意的是，ChatGPT 3.5 展现了高质量的翻译能力，而 SQLCoder 在文本到 SQL 任务上表现出色。该研究强调在阿拉伯语 NLP 任务中，融入语境架构并采用回译策略以提高模型性能的重要性。此外，提供了详细的方法可复现性和将数据集翻译为其他语言的方法，突显了该研究在促进透明度和协作知识共享方面的承诺。总的来说，这些贡献推动了 NLP 研究的发展，增强了阿拉伯语研究人员的能力，并丰富了全球关于语言理解和数据库查询的讨论。