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        <title>Meta-Learning a Cross-lingual Manifold for Semantic Parsing</title>
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    <abstract>Localizing a semantic parser to support new languages requires effective cross-lingual generalization. Recent work has found success with machine-translation or zero-shot methods, although these approaches can struggle to model how native speakers ask questions. We consider how to effectively leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer. Our algorithm uses high-resource languages to train the parser and simultaneously optimizes for cross-lingual generalization to lower-resource languages. Results across six languages on ATIS demonstrate that our combination of generalization steps yields accurate semantic parsers sampling łeq10% of source training data in each new language. Our approach also trains a competitive model on Spider using English with generalization to Chinese similarly sampling łeq10% of training data.1</abstract>
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    <identifier type="doi">10.1162/tacl_a_00533</identifier>
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        <date>2023</date>
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%0 Journal Article
%T Meta-Learning a Cross-lingual Manifold for Semantic Parsing
%A Sherborne, Tom
%A Lapata, Mirella
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F sherborne-lapata-2023-meta
%X Localizing a semantic parser to support new languages requires effective cross-lingual generalization. Recent work has found success with machine-translation or zero-shot methods, although these approaches can struggle to model how native speakers ask questions. We consider how to effectively leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer. Our algorithm uses high-resource languages to train the parser and simultaneously optimizes for cross-lingual generalization to lower-resource languages. Results across six languages on ATIS demonstrate that our combination of generalization steps yields accurate semantic parsers sampling łeq10% of source training data in each new language. Our approach also trains a competitive model on Spider using English with generalization to Chinese similarly sampling łeq10% of training data.1
%R 10.1162/tacl_a_00533
%U https://aclanthology.org/2023.tacl-1.4
%U https://doi.org/10.1162/tacl_a_00533
%P 49-67

Markdown (Informal)

[Meta-Learning a Cross-lingual Manifold for Semantic Parsing](https://aclanthology.org/2023.tacl-1.4) (Sherborne & Lapata, TACL 2023)

• Meta-Learning a Cross-lingual Manifold for Semantic Parsing (Sherborne & Lapata, TACL 2023)

ACL

• Tom Sherborne and Mirella Lapata. 2023. Meta-Learning a Cross-lingual Manifold for Semantic Parsing. Transactions of the Association for Computational Linguistics, 11:49–67.