@inproceedings{bisazza-federico-2009-morphological,
    title = "Morphological pre-processing for {T}urkish to {E}nglish statistical machine translation",
    author = "Bisazza, Arianna  and
      Federico, Marcello",
    booktitle = "Proceedings of the 6th International Workshop on Spoken Language Translation: Papers",
    month = dec # " 1-2",
    year = "2009",
    address = "Tokyo, Japan",
    url = "https://aclanthology.org/2009.iwslt-papers.1",
    pages = "129--135",
    abstract = "We tried to cope with the complex morphology of Turkish by applying different schemes of morphological word segmentation to the training and test data of a phrase-based statistical machine translation system. These techniques allow for a considerable reduction of the training dictionary, and lower the out-of-vocabulary rate of the test set. By minimizing differences between lexical granularities of Turkish and English we can produce more refined alignments and a better modeling of the translation task. Morphological segmentation is highly language dependent and requires a fair amount of linguistic knowledge in its development phase. Yet it is fast and light-weight {--} does not involve syntax {--} and appears to benefit our IWSLT09 system: our best segmentation scheme associated to a simple lexical approximation technique achieved a 50{\%} reduction of out-of-vocabulary rate and over 5 point BLEU improvement above the baseline.",
}