@inproceedings{chen-etal-2017-teacher,
    title = "A Teacher-Student Framework for Zero-Resource Neural Machine Translation",
    author = "Chen, Yun  and
      Liu, Yang  and
      Cheng, Yong  and
      Li, Victor O.K.",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P17-1176/",
    doi = "10.18653/v1/P17-1176",
    pages = "1925--1935",
    abstract = "While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language. Based on the assumption, our method is able to train a source-to-target NMT model ({\textquotedblleft}student{\textquotedblright}) without parallel corpora available guided by an existing pivot-to-target NMT model ({\textquotedblleft}teacher{\textquotedblright}) on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs."
}