@inproceedings{kann-schutze-2017-unlabeled,
    title = "Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models",
    author = {Kann, Katharina  and
      Sch{\"u}tze, Hinrich},
    editor = "Faruqui, Manaal  and
      Schuetze, Hinrich  and
      Trancoso, Isabel  and
      Yaghoobzadeh, Yadollah",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W17-4111/",
    doi = "10.18653/v1/W17-4111",
    pages = "76--81",
    abstract = "We present a semi-supervised way of training a character-based encoder-decoder recurrent neural network for morphological reinflection{---}the task of generating one inflected wordform from another. This is achieved by using unlabeled tokens or random strings as training data for an autoencoding task, adapting a network for morphological reinflection, and performing multi-task training. We thus use limited labeled data more effectively, obtaining up to 9.92{\%} improvement over state-of-the-art baselines for 8 different languages."
}