Abstract
We propose a morphology-based method for low-resource (LR) dependency parsing. We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create cross-lingual (x-inflected) treebanks that resemble the target LR language. We use such inflected treebanks to train parsers in zero- (training on x-inflected treebanks) and few-shot (training on x-inflected and target language treebanks) setups. The results show that the method sometimes improves the baselines, but not consistently.- Anthology ID:
- 2022.insights-1.7
- Volume:
- Proceedings of the Third Workshop on Insights from Negative Results in NLP
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- insights
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 54–61
- Language:
- URL:
- https://aclanthology.org/2022.insights-1.7
- DOI:
- 10.18653/v1/2022.insights-1.7
- Cite (ACL):
- Alberto Muñoz-Ortiz, Carlos Gómez-Rodríguez, and David Vilares. 2022. Cross-lingual Inflection as a Data Augmentation Method for Parsing. In Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 54–61, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Inflection as a Data Augmentation Method for Parsing (Muñoz-Ortiz et al., insights 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.insights-1.7.pdf