@inproceedings{gong-etal-2012-towards,
    title = "Towards contextual adaptation for any-text translation",
    author = "Gong, Li  and
      Max, Aur{\'e}lien  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 9th International Workshop on Spoken Language Translation: Papers",
    month = dec # " 6-7",
    year = "2012",
    address = "Hong Kong, Table of contents",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2012.iwslt-papers.20/",
    pages = "292--299",
    abstract = "Adaptation for Machine Translation has been studied in a variety of ways, using an ideal scenario where the training data can be split into {\textquotedblright}out-of-domain{\textquotedblright} and {\textquotedblright}in-domain{\textquotedblright} corpora, on which the adaptation is based. In this paper, we consider a more realistic setting which does not assume the availability of any kind of {\textquotedblright}in-domain{\textquotedblright} data, hence the name {\textquotedblright}any-text translation{\textquotedblright}. In this context, we present a new approach to contextually adapt a translation model onthe-fly, and present several experimental results where this approach outperforms conventionaly trained baselines. We also present a document-level contrastive evaluation whose results can be easily interpreted, even by non-specialists."
}