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.
@article{arxiv.2205.09350,
title = {Cross-lingual Inflection as a Data Augmentation Method for Parsing},
author = {Alberto Muñoz-Ortiz and Carlos Gómez-Rodríguez and David Vilares},
journal= {arXiv preprint arXiv:2205.09350},
year = {2022}
}
Comments
10 pages, 7 tables, 5 figures. Workshop on Insights from Negative Results in NLP 2022 (co-located with ACL)