@inproceedings{sorokin-2020-getting,
    title = "Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation",
    author = "Sorokin, Alexey",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.lrec-1.490/",
    pages = "3978--3983",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    abstract = "We investigate how to improve quality of low-resource morphological inflection without annotating more data. We examine two methods, language models and data augmentation. We show that the model whose decoder that additionally uses the states of the langauge model improves the model quality by 1.5{\%} in combination with both baselines. We also demonstrate that the augmentation of data improves performance by 9{\%} in average when adding 1000 artificially generated word forms to the dataset."
}