Abstract
While high performance have been obtained for high-resource languages, performance on low-resource languages lags behind. In this paper we focus on the parsing of the low-resource language Frisian. We use a sample of code-switched, spontaneously spoken data, which proves to be a challenging setup. We propose to train a parser specifically tailored towards the target domain, by selecting instances from multiple treebanks. Specifically, we use Latent Dirichlet Allocation (LDA), with word and character N-grams. We use a deep biaffine parser initialized with mBERT. The best single source treebank (nl_alpino) resulted in an LAS of 54.7 whereas our data selection outperformed the single best transfer treebank and led to 55.6 LAS on the test data. Additional experiments consisted of removing diacritics from our Frisian data, creating more similar training data by cropping sentences and running our best model using XLM-R. These experiments did not lead to a better performance.- Anthology ID:
- 2021.adaptnlp-1.6
- Volume:
- Proceedings of the Second Workshop on Domain Adaptation for NLP
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine
- Editors:
- Eyal Ben-David, Shay Cohen, Ryan McDonald, Barbara Plank, Roi Reichart, Guy Rotman, Yftah Ziser
- Venue:
- AdaptNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 50–58
- Language:
- URL:
- https://aclanthology.org/2021.adaptnlp-1.6
- DOI:
- Cite (ACL):
- Anouck Braggaar and Rob van der Goot. 2021. Challenges in Annotating and Parsing Spoken, Code-switched, Frisian-Dutch Data. In Proceedings of the Second Workshop on Domain Adaptation for NLP, pages 50–58, Kyiv, Ukraine. Association for Computational Linguistics.
- Cite (Informal):
- Challenges in Annotating and Parsing Spoken, Code-switched, Frisian-Dutch Data (Braggaar & van der Goot, AdaptNLP 2021)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2021.adaptnlp-1.6.pdf
- Code
- anouck96/parsingfrisian