@inproceedings{pal-heafield-2022-cheat,
    title = "Cheat Codes to Quantify Missing Source Information in Neural Machine Translation",
    author = "Pal, Proyag  and
      Heafield, Kenneth",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.naacl-main.177/",
    doi = "10.18653/v1/2022.naacl-main.177",
    pages = "2472--2477",
    abstract = "This paper describes a method to quantify the amount of information $H(t|s)$ added by the target sentence $t$ that is not present in the source $s$ in a neural machine translation system. We do this by providing the model the target sentence in a highly compressed form (a ``cheat code''), and exploring the effect of the size of the cheat code. We find that the model is able to capture extra information from just a single float representation of the target and nearly reproduces the target with two 32-bit floats per target token."
}