Abstract
More and more evidence is appearing that integrating symbolic lexical knowledge into neural models aids learning. This contrasts the widely-held belief that neural networks largely learn their own feature representations. For example, recent work has shows benefits of integrating lexicons to aid cross-lingual part-of-speech (PoS). However, little is known on how complementary such additional information is, and to what extent improvements depend on the coverage and quality of these external resources. This paper seeks to fill this gap by providing a thorough analysis on the contributions of lexical resources for cross-lingual PoS tagging in neural times.- Anthology ID:
- W19-6103
- Volume:
- Proceedings of the 22nd Nordic Conference on Computational Linguistics
- Month:
- September–October
- Year:
- 2019
- Address:
- Turku, Finland
- Editors:
- Mareike Hartmann, Barbara Plank
- Venue:
- NoDaLiDa
- SIG:
- Publisher:
- Linköping University Electronic Press
- Note:
- Pages:
- 25–34
- Language:
- URL:
- https://aclanthology.org/W19-6103
- DOI:
- Cite (ACL):
- Barbara Plank and Sigrid Klerke. 2019. Lexical Resources for Low-Resource PoS Tagging in Neural Times. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 25–34, Turku, Finland. Linköping University Electronic Press.
- Cite (Informal):
- Lexical Resources for Low-Resource PoS Tagging in Neural Times (Plank & Klerke, NoDaLiDa 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-6103.pdf