@inproceedings{munoz-ortiz-etal-2022-cross,
    title = "Cross-lingual Inflection as a Data Augmentation Method for Parsing",
    author = "Mu{\~n}oz-Ortiz, Alberto  and
      G{\'o}mez-Rodr{\'i}guez, Carlos  and
      Vilares, David",
    editor = "Tafreshi, Shabnam  and
      Sedoc, Jo{\~a}o  and
      Rogers, Anna  and
      Drozd, Aleksandr  and
      Rumshisky, Anna  and
      Akula, Arjun",
    booktitle = "Proceedings of the Third Workshop on Insights from Negative Results in NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.insights-1.7/",
    doi = "10.18653/v1/2022.insights-1.7",
    pages = "54--61",
    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."
}