@inproceedings{garcia-martinez-etal-2016-factored,
    title = "Factored Neural Machine Translation Architectures",
    author = {Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Barrault, Lo{\"\i}c  and
      Bougares, Fethi},
    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://aclanthology.org/2016.iwslt-1.3",
    abstract = "In this paper we investigate the potential of the neural machine translation (NMT) when taking into consideration the linguistic aspect of target language. From this standpoint, the NMT approach with attention mechanism [1] is extended in order to produce several linguistically derived outputs. We train our model to simultaneously output the lemma and its corresponding factors (e.g. part-of-speech, gender, number). The word level translation is built with a mapping function using a priori linguistic information. Compared to the standard NMT system, factored architecture increases significantly the vocabulary coverage while decreasing the number of unknown words. With its richer architecture, the Factored NMT approach allows us to implement several training setup that will be discussed in detail along this paper. On the IWSLT{'}15 English-to-French task, FNMT model outperforms NMT model in terms of BLEU score. A qualitative analysis of the output on a set of test sentences shows the effectiveness of the FNMT model.",
}