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- W4382202606 abstract "Text-to-SQL semantic parsing is an important NLP task, which facilitates the interaction between users and the database. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under various settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages." @default.
- W4382202606 created "2023-06-28" @default.
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- W4382202606 date "2023-06-26" @default.
- W4382202606 modified "2023-09-25" @default.
- W4382202606 title "MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing" @default.
- W4382202606 doi "https://doi.org/10.1609/aaai.v37i11.26499" @default.
- W4382202606 hasPublicationYear "2023" @default.
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