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In-context learning using large language models has recently shown surprisingresults for semantic parsing tasks such as Text-to-SQL translation. PromptingGPT-3 or Codex using several examples of question-SQL pairs can produceexcellent results, comparable to state-of-the-art finetuning-based models.However, existing work primarily focuses on English datasets, and it is unknownwhether large language models can serve as competitive semantic parsers forother languages. To bridge this gap, our work focuses on cross-lingualText-to-SQL semantic parsing for translating non-English utterances into SQLqueries based on an English schema. We consider a zero-shot transfer learningsetting with the assumption that we do not have any labeled examples in thetarget language (but have annotated examples in English). This work introducesthe XRICL framework, which learns to retrieve relevant English exemplars for agiven query to construct prompts. We also include global translation exemplarsfor a target language to facilitate the translation process for large languagemodels. To systematically evaluate our model, we construct two new benchmarkdatasets, XSpider and XKaggle-dbqa, which include questions in Chinese,Vietnamese, Farsi, and Hindi. Our experiments show that XRICL effectivelyleverages large pre-trained language models to outperform existing baselines.Data and code are publicly available at https://github.com/Impavidity/XRICL.

XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing