@inproceedings{jiang-etal-2012-monolingual,
    title = "Monolingual Data Optimisation for Bootstrapping {SMT} Engines",
    author = "Jiang, Jie  and
      Way, Andy  and
      Ng, Nelson  and
      Haque, Rejwanul  and
      Dillinger, Mike  and
      Lu, Jun",
    editor = "Okita, Tsuyoshi  and
      Sokolov, Artem  and
      Watanabe, Taro",
    booktitle = "Workshop on Monolingual Machine Translation",
    month = oct # " 28-" # nov # " 1",
    year = "2012",
    address = "San Diego, California, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2012.amta-monomt.2",
    abstract = "Content localisation via machine translation (MT) is a sine qua non, especially for international online business. While most applications utilise rule-based solutions due to the lack of suitable in-domain parallel corpora for statistical MT (SMT) training, in this paper we investigate the possibility of applying SMT where huge amounts of monolingual content only are available. We describe a case study where an analysis of a very large amount of monolingual online trading data from eBay is conducted by ALS with a view to reducing this corpus to the most representative sample in order to ensure the widest possible coverage of the total data set. Furthermore, minimal yet optimal sets of sentences/words/terms are selected for generation of initial translation units for future SMT system-building.",
}