@inproceedings{majithia-etal-2005-rapid,
    title = "Rapid Ramp-up for Statistical Machine Translation: Minimal Training for Maximal Coverage",
    author = "Majithia, Hemali  and
      Rennart, Philip  and
      Tzoukermann, Evelyne",
    booktitle = "Proceedings of Machine Translation Summit X: Posters",
    month = sep # " 13-15",
    year = "2005",
    address = "Phuket, Thailand",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2005.mtsummit-posters.17/",
    pages = "438--444",
    abstract = "This paper investigates optimal ways to get maximal coverage from minimal input training corpus. In effect, it seems antagonistic to think of minimal input training with a statistical machine translation system. Since statistics work well with repetition and thus capture well highly occurring words, one challenge has been to figure out the optimal number of {\textquotedblleft}new{\textquotedblright} words that the system needs to be appropriately trained. Additionally, the goal is to minimize the human translation time for training a new language. In order to account for rapid ramp-up translation, we ran several experiments to figure out the minimal amount of data to obtain optimal translation results."
}