arXiv 1605.091862016-08-16cs.CLcs.LGcs.NE

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

LIUM, University of Le Mans（勒芒大学LIUM）；Galatasaray University（加拉塔萨雷大学）；CVC, Universitat Autonoma de Barcelona（巴塞罗那自治大学CVC）

Ozan Caglayan, Walid Aransa, Yaxing Wang, Marc Masana, Mercedes García-Martínez, Fethi Bougares, Loïc Barrault, Joost van de Weijer

备注：: 7 pages, 2 figures, v4: Small clarification in section 4 title and content
DOI：: 10.18653/v1/W16-2358

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英文摘要：

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 the automatic evaluation metrics BLEU and METEOR.

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