@inproceedings{brown-2008-exploiting,
    title = "Exploiting Document-Level Context for Data-Driven Machine Translation",
    author = "Brown, Ralf",
    booktitle = "Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers",
    month = oct # " 21-25",
    year = "2008",
    address = "Waikiki, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/ingest-emnlp/2008.amta-papers.2/",
    pages = "46--55",
    abstract = "This paper presents a method for exploiting document-level similarity between the documents in the training corpus for a corpus-driven (statistical or example-based) machine translation system and the input documents it must translate. The method is simple to implement, efficient (increases the translation time of an example-based system by only a few percent), and robust (still works even when the actual document boundaries in the input text are not known). Experiments on French-English and Arabic-English showed relative gains over the same system without using document-level similarity of up to 7.4{\%} and 5.4{\%}, respectively, on the BLEU metric."
}