@inproceedings{gupta-etal-2011-extending,
    title = "Extending a probabilistic phrase alignment approach for {SMT}",
    author = "Gupta, Mridul  and
      Hewavitharana, Sanjika  and
      Vogel, Stephan",
    editor = {Federico, Marcello  and
      Hwang, Mei-Yuh  and
      R{\"o}dder, Margit  and
      St{\"u}ker, Sebastian},
    booktitle = "Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign",
    month = dec # " 8-9",
    year = "2011",
    address = "San Francisco, California",
    url = "https://preview.aclanthology.org/fix-sig-urls/2011.iwslt-evaluation.23/",
    pages = "175--182",
    abstract = "Phrase alignment is a crucial step in phrase-based statistical machine translation. We explore a way of improving phrase alignment by adding syntactic information in the form of chunks as soft constraints guided by an in-depth and detailed analysis on a hand-aligned data set. We extend a probabilistic phrase alignment model that extracts phrase pairs by optimizing phrase pair boundaries over the sentence pair [1]. The boundaries of the target phrase are chosen such that the overall sentence alignment probability is optimal. Viterbi alignment information is also added in the extended model with a view of improving phrase alignment. We extract phrase pairs using a relatively larger number of features which are discriminatively trained using a large-margin online learning algorithm, i.e., Margin Infused Relaxed Algorithm (MIRA) and integrate it in our approach. Initial experiments show improvements in both phrase alignment and translation quality for Arabic-English on a moderate-size translation task."
}