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Link to original content: https://doi.org/10.48550/arXiv.1708.09157
[1708.09157] Cross-lingual, Character-Level Neural Morphological Tagging
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Computer Science > Computation and Language

arXiv:1708.09157 (cs)
[Submitted on 30 Aug 2017 (v1), last revised 6 Jun 2024 (this version, v5)]

Title:Cross-lingual, Character-Level Neural Morphological Tagging

Authors:Ryan Cotterell, Georg Heigold
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Abstract:Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones, improving accuracy by up to 30% over a monolingual model.
Comments: Published as a conference paper at EMNLP 2017; Fixed minor typos and cleaned up formatting
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:1708.09157 [cs.CL]
  (or arXiv:1708.09157v5 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.1708.09157

Submission history

From: Ryan Cotterell [view email]
[v1] Wed, 30 Aug 2017 08:14:34 UTC (729 KB)
[v2] Thu, 4 Jul 2019 13:53:42 UTC (729 KB)
[v3] Mon, 13 Jan 2020 10:30:21 UTC (729 KB)
[v4] Wed, 29 May 2024 22:05:22 UTC (792 KB)
[v5] Thu, 6 Jun 2024 08:27:54 UTC (791 KB)
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