PT Unknown
AU Ozan Caglayan
   Walid Aransa
   Yaxing Wang
   Marc Masana
   Mercedes Garcıa-Martinez
   Fethi Bougares
   Loic Barrault
   Joost Van de Weijer
TI Does Multimodality Help Human and Machine for Translation and Image Captioning?
BT 1st conference on machine translation
PY 2016
AB This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR.
ER