Does Multimodality Help Human and Machine for Translation and Image Captioning?

@inproceedings{Caglayan2016DoesMH,
  title={Does Multimodality Help Human and Machine for Translation and Image Captioning?},
  author={Ozan Caglayan and Walid Aransa and Yaxing Wang and Marc Masana and Mercedes Garc{\'i}a-Mart{\'i}nez and Fethi Bougares and Lo{\"i}c Barrault and Joost van de Weijer},
  booktitle={WMT},
  year={2016}
}
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 the usefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to… CONTINUE READING
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