@inproceedings{mauser-etal-2006-training,
    title = "Training a Statistical Machine Translation System without {GIZA}++",
    author = "Mauser, Arne  and
      Matusov, Evgeny  and
      Ney, Hermann",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Gangemi, Aldo  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Odijk, Jan  and
      Tapias, Daniel",
    booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}`06)",
    month = may,
    year = "2006",
    address = "Genoa, Italy",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/L06-1184/",
    abstract = "The IBM Models (Brown et al., 1993) enjoy great popularity in the machine translation community because they offer high quality word alignments and a free implementation is available with the GIZA++ Toolkit (Och and Ney, 2003). Several methods have been developed to overcome the asymmetry of the alignment generated by the IBM Models. A remaining disadvantage, however, is the high model complexity. This paper describes a word alignment training procedure for statistical machine translation that uses a simple and clear statistical model, different from the IBM models. The main idea of the algorithm is to generate a symmetric and monotonic alignment between the target sentence and a permutation graph representing different reorderings of the words in the source sentence. The quality of the generated alignment is shown to be comparable to the standard GIZA++ training in an SMT setup."
}