Abstract
We tackle the important task of part-of-speech tagging using a neural model in the zero-resource scenario, where we have no access to gold-standard POS training data. We compare this scenario with the low-resource scenario, where we have access to a small amount of gold-standard POS training data. Our experiments focus on Ukrainian as a representative of under-resourced languages. Russian is highly related to Ukrainian, so we exploit gold-standard Russian POS tags. We consider four techniques to perform Ukrainian POS tagging: zero-shot tagging and cross-lingual annotation projection (for the zero-resource scenario), and compare these with self-training and multilingual learning (for the low-resource scenario). We find that cross-lingual annotation projection works particularly well in the zero-resource scenario.- Anthology ID:
- W19-1425
- Volume:
- Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
- Month:
- June
- Year:
- 2019
- Address:
- Ann Arbor, Michigan
- Editors:
- Marcos Zampieri, Preslav Nakov, Shervin Malmasi, Nikola Ljubešić, Jörg Tiedemann, Ahmed Ali
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 223–233
- Language:
- URL:
- https://aclanthology.org/W19-1425
- DOI:
- 10.18653/v1/W19-1425
- Cite (ACL):
- Matthias Huck, Diana Dutka, and Alexander Fraser. 2019. Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 223–233, Ann Arbor, Michigan. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging (Huck et al., VarDial 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-1425.pdf