Cross-lingual Inflection as a Data Augmentation Method for Parsing
This addresses parsing for low-resource languages, but it is incremental as it builds on existing methods with mixed success.
The paper tackled low-resource dependency parsing by using cross-lingual inflection as data augmentation, but the results showed inconsistent improvements over baselines.
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.