Tag: WMT16
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WMT16 paper accepted
Our paper “Does Multimodality Help Human and Machine for Translation and Image Captioning?” has been accepted on the ACL 2016 First Conference on Machine Translation. Check some results here.
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Does Multimodality Help Human and Machine for Translation and Image Captioning?
Ozan Caglayan, Walid Aransa, Yaxing Wang, Marc Masana, Mercedes García-Martínez, Fethi Bougares, Loïc Barrault, Joost van de Weijer Read Full Paper → 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 […]