@inproceedings{xu-etal-2020-dual,
    title = "Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation",
    author = "Xu, Weijia  and
      Niu, Xing  and
      Carpuat, Marine",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.findings-emnlp.182/",
    doi = "10.18653/v1/2020.findings-emnlp.182",
    pages = "2006--2020",
    abstract = "While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. We introduce a novel dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. It motivates a theoretical analysis and controlled empirical study on German-English and Turkish-English tasks, which both suggest that Iterative Back-Translation is more effective than Dual Learning despite its relative simplicity."
}