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Zero-Shot Dependenc...
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning
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- de Lhoneux, Miryam, 1990- (författare)
- Uppsala universitet,Institutionen för lingvistik och filologi,Univ Copenhagen, Copenhagen, Denmark.;Katholieke Univ Leuven, Leuven, Belgium.
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- Zhang, Sheng (författare)
- Natl Univ Def Technol, Changsha, Peoples R China.
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- Sogaard, Anders (författare)
- Univ Copenhagen, Copenhagen, Denmark.
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(creator_code:org_t)
- Stroudsburg, PA, USA : Association for Computational Linguistics, 2022
- 2022
- Engelska.
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Ingår i: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Acl 2022). - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781955917223 ; , s. 578-587
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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