TY  - CONF
AU  - Ozan Caglayan
AU  - Walid Aransa
AU  - Yaxing Wang
AU  - Marc Masana
AU  - Mercedes Garcıa-Martinez
AU  - Fethi Bougares
AU  - Loic Barrault
AU  - Joost Van de Weijer
A2  - WMT
PY  - 2016//
TI  - Does Multimodality Help Human and Machine for Translation and Image Captioning?
BT  - 1st conference on machine translation
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/CAW2016.pdf
N1  - LAMP; 600.106 ; 600.068
ID  - Ozan Caglayan2016
ER  -