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Semantic parsing (SP) allows humans to leverage vast knowledge resourcesthrough natural interaction. However, parsers are mostly designed for andevaluated on English resources, such as CFQ (Keysers et al., 2020), the currentstandard benchmark based on English data generated from grammar rules andoriented towards Freebase, an outdated knowledge base. We propose a method forcreating a multilingual, parallel dataset of question-query pairs, grounded inWikidata. We introduce such a dataset, which we call Multilingual CompositionalWikidata Questions (MCWQ), and use it to analyze the compositionalgeneralization of semantic parsers in Hebrew, Kannada, Chinese and English.While within-language generalization is comparable across languages,experiments on zero-shot cross-lingual transfer demonstrate that cross-lingualcompositional generalization fails, even with state-of-the-art pretrainedmultilingual encoders. Furthermore, our methodology, dataset and results willfacilitate future research on SP in more realistic and diverse settings thanhas been possible with existing resources.

Compositional Generalization in Multilingual Semantic Parsing over Wikidata