@article{sherborne-lapata-2023-meta,
    title = "Meta-Learning a Cross-lingual Manifold for Semantic Parsing",
    author = "Sherborne, Tom  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "11",
    year = "2023",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.tacl-1.4/",
    doi = "10.1162/tacl_a_00533",
    pages = "49--67",
    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 {\ensuremath{\leq}}10{\%} 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 {\ensuremath{\leq}}10{\%} of training data.1"
}