Compositional Generalization in Multilingual Semantic Parsing over Wikidata

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Semantic parsing (SP) allows humans to lever-age vast knowledge resources through natural interaction. However, parsers are mostly de-signed 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the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base.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 encod-ers. 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.

TidsskriftTransactions of the Association for Computational Linguistics
Sider (fra-til)937-955
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
The authors thank Anders Søgaard and Miryam de Lhoneux for their comments and suggestions, as well as the TACL editors and several rounds of reviewers for their constructive evaluation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 801199 (Heather Lent).

Publisher Copyright:
© 2022 Association for Computational Linguistics.

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