@inproceedings{dehouck-gomez-rodriguez-2020-data,
    title = "Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages",
    author = "Dehouck, Mathieu  and
      G{\'o}mez-Rodr{\'i}guez, Carlos",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.coling-main.339/",
    doi = "10.18653/v1/2020.coling-main.339",
    pages = "3818--3830",
    abstract = "The lack of annotated data is a big issue for building reliable NLP systems for most of the world{'}s languages. But this problem can be alleviated by automatic data generation. In this paper, we present a new data augmentation method for artificially creating new dependency-annotated sentences. The main idea is to swap subtrees between annotated sentences while enforcing strong constraints on those trees to ensure maximal grammaticality of the new sentences. We also propose a method to perform low-resource experiments using resource-rich languages by mimicking low-resource languages by sampling sentences under a low-resource distribution. In a series of experiments, we show that our newly proposed data augmentation method outperforms previous proposals using the same basic inputs."
}