@inproceedings{carter-monz-2010-discriminative,
    title = "Discriminative Syntactic Reranking for Statistical Machine Translation",
    author = "Carter, Simon  and
      Monz, Christof",
    booktitle = "Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers",
    month = oct # " 31-" # nov # " 4",
    year = "2010",
    address = "Denver, Colorado, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/ingest-emnlp/2010.amta-papers.1/",
    abstract = "This paper describes a method that successfully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine translation system. The performance is evaluated for Arabic-to-English translation using NIST{'}s MT-Eval benchmarks. Whilst parse trees do not consistently help, we show how features extracted from a simple Part-of-Speech annotation layer outperform two competitive baselines, leading to significant BLEU improvements on three different test sets."
}