Compositional Generalization in Multilingual Semantic Parsing over Wikidata
Published
2022-09-07
Ruixiang Cui
,
Rahul Aralikatte
,
Heather Lent
,
Daniel Hershcovich
Ruixiang Cui
University of Copenhagen
Rahul Aralikatte
University of Copenhagen
Heather Lent
University of Copenhagen
Daniel Hershcovich
University of Copenhagen
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the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base.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.
Article at MIT Press
Presented at EMNLP 2022
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