@inproceedings{jin-kann-2017-exploring,
    title = "Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models",
    author = "Jin, Huiming  and
      Kann, Katharina",
    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/W17-4110/",
    doi = "10.18653/v1/W17-4110",
    pages = "70--75",
    abstract = "Multi-task training is an effective method to mitigate the data sparsity problem. It has recently been applied for cross-lingual transfer learning for paradigm completion{---}the task of producing inflected forms of lemmata{---}with sequence-to-sequence networks. However, it is still vague how the model transfers knowledge across languages, as well as if and which information is shared. To investigate this, we propose a set of data-dependent experiments using an existing encoder-decoder recurrent neural network for the task. Our results show that indeed the performance gains surpass a pure regularization effect and that knowledge about language and morphology can be transferred."
}