Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning

• URL: http://arxiv.org/abs/2203.08555v1
• Date: Wed, 16 Mar 2022 11:33:20 GMT
• Title: Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning
• Authors: Miryam de Lhoneux, Sheng Zhang and Anders S{\o}gaard
• Abstract summary: We adopt a method from multi-task learning, which relies on automated curriculum learning, to dynamically optimize for parsing performance on outlier languages. We show that this approach is significantly better than uniform and size-proportional sampling in the zero-shot setting.
• Score: 5.865807597752895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be surprisingly effective for cross-lingual transfer of syntactic parsing models (Wu and Dredze 2019), but only between related languages. However, source and training languages are rarely related, when parsing truly low-resource languages. To close this gap, we adopt a method from multi-task learning, which relies on automated curriculum learning, to dynamically optimize for parsing performance on outlier languages. We show that this approach is significantly better than uniform and size-proportional sampling in the zero-shot setting.

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