@inproceedings{zhang-sennrich-2021-edinburghs,
    title = "{E}dinburgh`s End-to-End Multilingual Speech Translation System for {IWSLT} 2021",
    author = "Zhang, Biao  and
      Sennrich, Rico",
    editor = "Federico, Marcello  and
      Waibel, Alex  and
      Costa-juss{\`a}, Marta R.  and
      Niehues, Jan  and
      Stuker, Sebastian  and
      Salesky, Elizabeth",
    booktitle = "Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)",
    month = aug,
    year = "2021",
    address = "Bangkok, Thailand (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.iwslt-1.19/",
    doi = "10.18653/v1/2021.iwslt-1.19",
    pages = "160--168",
    abstract = "This paper describes Edinburgh`s submissions to the IWSLT2021 multilingual speech translation (ST) task. We aim at improving multilingual translation and zero-shot performance in the constrained setting (without using any extra training data) through methods that encourage transfer learning and larger capacity modeling with advanced neural components. We build our end-to-end multilingual ST model based on Transformer, integrating techniques including adaptive speech feature selection, language-specific modeling, multi-task learning, deep and big Transformer, sparsified linear attention and root mean square layer normalization. We adopt data augmentation using machine translation models for ST which converts the zero-shot problem into a zero-resource one. Experimental results show that these methods deliver substantial improvements, surpassing the official baseline by {\ensuremath{>}} 15 average BLEU and outperforming our cascading system by {\ensuremath{>}} 2 average BLEU. Our final submission achieves competitive performance (runner up)."
}