MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing

• URL: http://arxiv.org/abs/2212.13492v1
• Date: Tue, 27 Dec 2022 13:58:30 GMT
• Title: MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing
• Authors: Longxu Dou, Yan Gao, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Jian-Guang Lou
• Abstract summary: We present MultiSpider, the largest multilingual text-to- schema- dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese) Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. We also propose a simple framework augmentation framework SAVe (Augmentation-with-Verification) which boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.
• Score: 48.216386761482525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. 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 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 three typical 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.

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