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The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task ...
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The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task
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In: http://infoscience.epfl.ch/record/289182 (2021)
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Treebank embedding vectors for out-of-domain dependency parsing
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In: Wagner, Joachim orcid:0000-0002-8290-3849 , Barry, James orcid:0000-0003-3051-585X and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) Treebank embedding vectors for out-of-domain dependency parsing. In: 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 05-10 Jul 2020, Online (virtual conference). (2020)
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Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study
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In: Barry, James orcid:0000-0003-3051-585X , Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2019) Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study. In: The 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), 3 - 5 Nov 2019, Hong Kong, China. ISBN 978-1-950737-78-9 (2019)
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Free Ocean CO2 Enrichment (FOCE) experiments: Scientific and technical recommendations for future in situ ocean acidification projects
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In: ISSN: 0079-6611 ; Progress in Oceanography ; https://hal.archives-ouvertes.fr/hal-02332356 ; Progress in Oceanography, Elsevier, 2019, 172, pp.89-107. ⟨10.1016/j.pocean.2019.01.006⟩ (2019)
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Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study ...
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Abstract:
Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. It is a crucial component for inducing dependency parsers in low-resource scenarios where no training data for a language exists. Using Faroese as the target language, we compare two approaches using annotation projection: first, projecting from multiple monolingual source models; second, projecting from a single polyglot model which is trained on the combination of all source languages. Furthermore, we reproduce multi-source projection (Tyers et al., 2018), in which dependency trees of multiple sources are combined. Finally, we apply multi-treebank modelling to the projected treebanks, in addition to or alternatively to polyglot modelling on the source side. We find that polyglot training on the source languages produces an overall trend of better results on the target language but the single best result for the target language is obtained by projecting from monolingual source parsing models and then ... : Submitted to the DeepLo workshop at EMNLP ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1910.07938
https://dx.doi.org/10.48550/arxiv.1910.07938
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