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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 \u226410% 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 \u226410% of training data.1<\/jats:p>","DOI":"10.1162\/tacl_a_00533","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T16:21:30Z","timestamp":1674577290000},"page":"49-67","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":6,"title":["Meta-Learning a Cross-lingual Manifold for Semantic Parsing"],"prefix":"10.1162","volume":"11","author":[{"given":"Tom","family":"Sherborne","sequence":"first","affiliation":[{"name":"Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB, UK. tom.sherborne@ed.ac.uk"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirella","family":"Lapata","sequence":"additional","affiliation":[{"name":"Institute for Language, Cognition and 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