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    <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.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning
%A de Lhoneux, Miryam
%A Zhang, Sheng
%A Søgaard, Anders
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F de-lhoneux-etal-2022-zero
%X 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.
%R 10.18653/v1/2022.acl-short.64
%U https://aclanthology.org/2022.acl-short.64/
%U https://doi.org/10.18653/v1/2022.acl-short.64
%P 578-587

Markdown (Informal)

[Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning](https://aclanthology.org/2022.acl-short.64/) (de Lhoneux et al., ACL 2022)

• Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning (de Lhoneux et al., ACL 2022)

ACL

• Miryam de Lhoneux, Sheng Zhang, and Anders Søgaard. 2022. Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 578–587, Dublin, Ireland. Association for Computational Linguistics.