mRAT-SQL+GAP: A Portuguese Text-to-SQL Transformer

José, Marcelo Archanjo; Cozman, Fábio Gagliardi

doi:10.1007/978-3-030-91699-2_35

Cited by 6 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These corpora cover a wide range of settings: single-table (Zhong, Xiong, and Socher 2017), multi-table (Yu et al 2018), multi-turn (Yu et al 2019a,b). There are also a few non-English text-to-SQL datasets (Min and Zhang 2019;Tuan Nguyen, Dao, and Nguyen 2020;Guo et al 2021;José and Cozman 2021).…”

Section: Multilingual Text-to-sql Datasetsmentioning

confidence: 99%

“…Several works attempted to extend to new languages, but currently available multilingual text-to-SQL datasets only support four languages (English, Chinese, and Vietnamese and Portuguese) (Yu et al 2018;Min and Zhang 2019;Tuan Nguyen, Dao, and Nguyen 2020;José and Cozman 2021), which hinders the study of multilingual text-to-SQL across a broad spectrum of language distances. Besides the language coverage, the existing multilingual datasets also suffer from the following limitations: (1) low-quality: unnatural or inaccurate translations; (2) in-completed translation: the database of Chinese-Spider and Portuguese-Spider are not translated and kept in English.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Longxu

Gao

Pan

et al. 2023

AAAI

View full text Add to dashboard Cite

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.

show abstract