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.- Anthology ID:
- D17-1078
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 748–759
- Language:
- URL:
- https://aclanthology.org/D17-1078
- DOI:
- 10.18653/v1/D17-1078
- Cite (ACL):
- Ryan Cotterell and Georg Heigold. 2017. Cross-lingual Character-Level Neural Morphological Tagging. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 748–759, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Character-Level Neural Morphological Tagging (Cotterell & Heigold, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D17-1078.pdf
- Data
- Universal Dependencies