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Parameter sharing between dependency parsers for related languages
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Computational Linguistics)ORCID iD: 0000-0001-8844-2126
University of Copenhagen.
University of Copenhagen.
University of Copenhagen.
2018 (English)In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing / [ed] Association for Computational Linguistics, Brussels, 2018, p. 4992-4997Conference paper, Published paper (Refereed)
Abstract [en]

Previous work has suggested that parameter sharing between transition-based neural dependency parsers for related languages can lead to better performance, but there is no consensus on what parameters to share. We present an evaluation of 27 different parameter sharing strategies across 10 languages, representing five pairs of related languages, each pair from a different language family. We find that sharing transition classifier parameters always helps, whereas the usefulness of sharing word and/or character LSTM parameters varies. Based on this result, we propose an architecture where the transition classifier is shared, and the sharing of word and character parameters is controlled by a parameter that can be tuned on validation data. This model is linguistically motivated and obtains significant improvements over a mono-lingually trained baseline. We also find that sharing transition classifier parameters helps when training a parser on unrelated language pairs, but we find that, in the case of unrelated languages, sharing too many parameters does not help.

Place, publisher, year, edition, pages
Brussels, 2018. p. 4992-4997
Keywords [en]
parameter sharing, dependency parsing, multilingual parsing
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:uu:diva-365768OAI: oai:DiVA.org:uu-365768DiVA, id: diva2:1262960
Conference
2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2018-11-14Bibliographically approved

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http://aclweb.org/anthology/D18-1543

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de Lhoneux, Miryam

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf