Compositional Generalization in Multilingual Semantic Parsing over Wikidata

Ruixiang Cui, Rahul Aralikatte, Heather Lent, Daniel Hershcovich


Abstract
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the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base.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.
Anthology ID:
2022.tacl-1.55
Volume:
Transactions of the Association for Computational Linguistics, Volume 10
Month:
Year:
2022
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
937–955
Language:
URL:
https://aclanthology.org/2022.tacl-1.55
DOI:
10.1162/tacl_a_00499
Bibkey:
Cite (ACL):
Ruixiang Cui, Rahul Aralikatte, Heather Lent, and Daniel Hershcovich. 2022. Compositional Generalization in Multilingual Semantic Parsing over Wikidata. Transactions of the Association for Computational Linguistics, 10:937–955.
Cite (Informal):
Compositional Generalization in Multilingual Semantic Parsing over Wikidata (Cui et al., TACL 2022)
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PDF:
https://aclanthology.org/2022.tacl-1.55.pdf