@inproceedings{nadejde-etal-2016-neural,
    title = "A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation",
    author = "N{\u{a}}dejde, Maria  and
      Birch, Alexandra  and
      Koehn, Philipp",
    editor = {Cettolo, Mauro  and
      Niehues, Jan  and
      St{\"u}ker, Sebastian  and
      Bentivogli, Luisa  and
      Cattoni, Rolando  and
      Federico, Marcello},
    booktitle = "Proceedings of the 13th International Conference on Spoken Language Translation",
    month = dec # " 8-9",
    year = "2016",
    address = "Seattle, Washington D.C",
    publisher = "International Workshop on Spoken Language Translation",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2016.iwslt-1.11/",
    abstract = "String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5{\%}. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model`s accuracy by 1.5{\%} over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7{\%}."
}