@inproceedings{son-lyu-2020-sparse,
    title = "Sparse and Decorrelated Representations for Stable Zero-shot {NMT}",
    author = "Son, Bokyung  and
      Lyu, Sungwon",
    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/ingest_wac_2008/2020.findings-emnlp.205/",
    doi = "10.18653/v1/2020.findings-emnlp.205",
    pages = "2260--2266",
    abstract = "Using a single encoder and decoder for all directions and training with English-centric data is a popular scheme for multilingual NMT. However, zero-shot translation under this scheme is vulnerable to changes in training conditions, as the model degenerates by decoding non-English texts into English regardless of the target specifier token. We present that enforcing both sparsity and decorrelation on encoder intermediate representations with the SLNI regularizer (Aljundi et al., 2019) efficiently mitigates this problem, without performance loss in supervised directions. Notably, effects of SLNI turns out to be irrelevant to promoting language-invariance in encoder representations."
}