@article{kiperwasser-ballesteros-2018-scheduled,
    title = "Scheduled Multi-Task Learning: From Syntax to Translation",
    author = "Kiperwasser, Eliyahu  and
      Ballesteros, Miguel",
    editor = "Lee, Lillian  and
      Johnson, Mark  and
      Toutanova, Kristina  and
      Roark, Brian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/Q18-1017/",
    doi = "10.1162/tacl_a_00017",
    pages = "225--240",
    abstract = "Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner. We propose a framework in which our model begins learning syntax and translation interleaved, gradually putting more focus on translation. Using this approach, we achieve considerable improvements in terms of BLEU score on relatively large parallel corpus (WMT14 English to German) and a low-resource (WIT German to English) setup."
}