@inproceedings{farajian-etal-2016-fbks,
    title = "{FBK}{'}s Neural Machine Translation Systems for {IWSLT} 2016",
    author = "Farajian, M. Amin  and
      Chatterjee, Rajen  and
      Conforti, Costanza  and
      Jalalvand, Shahab  and
      Balaraman, Vevake  and
      Di Gangi, Mattia A.  and
      Ataman, Duygu  and
      Turchi, Marco  and
      Negri, Matteo  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://aclanthology.org/2016.iwslt-1.15",
    abstract = "In this paper, we describe FBK{'}s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-{de, fr} and {de, fr}-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs.",
}