Muñoz-Ortiz, AlbertoGómez-Rodríguez, CarlosVilares, David2024-05-272024-05-272022-05Alberto 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.http://hdl.handle.net/2183/36647Held 26 May 2022, Dublin, Ireland.[Absctract]: 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.engAtribución 3.0 Españahttp://creativecommons.org/licenses/by/3.0/es/Cross-lingual inflectionMorphological InflectionData augmentationDependency parsingLow-resource languagesSyntactic data augmentationconference outputopen access