@inproceedings{li-etal-2020-end,
    title = "End-to-End Speech Translation with Adversarial Training",
    author = "Li, Xuancai  and
      Kehai, Chen  and
      Zhao, Tiejun  and
      Yang, Muyun",
    editor = "Wu, Hua  and
      Cherry, Colin  and
      Huang, Liang  and
      He, Zhongjun  and
      Liberman, Mark  and
      Cross, James  and
      Liu, Yang",
    booktitle = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2020.autosimtrans-1.2/",
    doi = "10.18653/v1/2020.autosimtrans-1.2",
    pages = "10--14",
    abstract = "End-to-End speech translation usually leverages audio-to-text parallel data to train an available speech translation model which has shown impressive results on various speech translation tasks. Due to the artificial cost of collecting audio-to-text parallel data, the speech translation is a natural low-resource translation scenario, which greatly hinders its improvement. In this paper, we proposed a new adversarial training method to leverage target monolingual data to relieve the low-resource shortcoming of speech translation. In our method, the existing speech translation model is considered as a Generator to gain a target language output, and another neural Discriminator is used to guide the distinction between outputs of speech translation model and true target monolingual sentences. Experimental results on the CCMT 2019-BSTC dataset speech translation task demonstrate that the proposed methods can significantly improve the performance of the End-to-End speech translation system."
}