mlsanityData to 7 Oct 2026

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

arXiv 2203.08555 ↗First posted 16 Mar 2022cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 37 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

16 of 37 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  4. 2019How multilingual is Multilingual BERT?cited by 642
  5. 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
  6. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  7. 2019Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTcited by 301
  8. 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 269
  9. 2017Automated Curriculum Learning for Neural Networkscited by 251
  10. 2019Fairness risk measurescited by 85
  11. 2020UDapter: Language Adaptation for Truly Universal Dependency Parsingcited by 77
  12. 2020Zero-Shot Cross-Lingual Transfer with Meta Learningcited by 66
4 more
  1. 2020Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLPcited by 50
  2. 2021Minimax and Neyman-Pearson Meta-Learning for Outlier Languagescited by 10
  3. 2020Worst-Case-Aware Curriculum Learning for Zero and Few Shot Transfercited by 6
  4. 2021Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfercited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

5 papers.

  1. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  2. 2022CLNode: Curriculum Learning for Node Classificationcited by 24
  3. 2024The Zeno's Paradox of `Low-Resource' Languagescited by 12
  4. 2023Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalizationcited by 6
  5. 2026Zero-shot Dependency Parsing with Unsupervised Cross-Lingual Bootstrappingcited by 0