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Zero-Shot Cross-lingual Semantic Parsing

2021-04-15 · ACL 2022 5 · Tom Sherborne, Mirella Lapata

Recent work in cross-lingual semantic parsing has successfully applied machine translation to localize parsers to new languages. However, these advances assume access to high-quality machine translation systems and word alignment tools. We remove these assumptions and study cross-lingual semantic parsing as a zero-shot problem, without parallel data (i.e., utterance-logical form pairs) for new languages. We propose a multi-task encoder-decoder model to transfer parsing knowledge to additional languages using only English-logical form paired data and in-domain natural language corpora in each new language. Our model encourages language-agnostic encodings by jointly optimizing for logical-form generation with auxiliary objectives designed for cross-lingual latent representation alignment. Our parser performs significantly above translation-based baselines and, in some cases, competes with the supervised upper-bound.

📄 PDF Abstract BibTeX arXiv:2104.07554

Code (1)

tomsherborne/zx-parse 공식 구현 pytorch

Tasks

Cross-Lingual TransferDecoderFormMachine TranslationSemantic ParsingTranslationWord Alignment

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