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Author (up) Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer edit   pdf
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  Title Does Multimodality Help Human and Machine for Translation and Image Captioning? Type Conference Article
  Year 2016 Publication 1st conference on machine translation Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract 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.  
  Address Berlin; Germany; August 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WMT  
  Notes LAMP; 600.106 ; 600.068 Approved no  
  Call Number Admin @ si @ CAW2016 Serial 2761  
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