@inproceedings{sahin-steedman-2018-data,
    title = "Data Augmentation via Dependency Tree Morphing for Low-Resource Languages",
    author = {{\c{S}}ahin, G{\"o}zde G{\"u}l  and
      Steedman, Mark},
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/D18-1545/",
    doi = "10.18653/v1/D18-1545",
    pages = "5004--5009",
    abstract = "Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We {\textquotedblleft}crop{\textquotedblright} sentences by removing dependency links, and we {\textquotedblleft}rotate{\textquotedblright} sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems."
}