Semantic parsing (SP) allows humans to leverage vast knowledge resources
through natural interaction. However, parsers are mostly designed for and
evaluated on English resources, such as CFQ (Keysers et al., 2020), the current
standard benchmark based on English data generated from grammar rules and
oriented towards Freebase, an outdated knowledge base. We propose a method for
creating a multilingual, parallel dataset of question-query pairs, grounded in
Wikidata. We introduce such a dataset, which we call Multilingual Compositional
Wikidata Questions (MCWQ), and use it to analyze the compositional
generalization 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-lingual
compositional generalization fails, even with state-of-the-art pretrained
multilingual encoders. Furthermore, our methodology, dataset and results will
facilitate future research on SP in more realistic and diverse settings than
has been possible with existing resources.

本篇文章提出了一种基于 Wikidata 的多语言、平行的问句对数据集，即 Multilingual Compositional Wikidata Questions（MCWQ），用于分析语义解析器在英语、希伯来语、卡纳达语和汉语中的组合泛化能力。结果表明，即使使用最先进的预训练多语言编码器，跨语言组合泛化能力也无法实现，文章的方法、数据集和结果将有助于未来关于具有更现实和多样化背景下语义解析的研究。