Zero-resource Machine Translation by Multimodal Encoder-decoder Network with Multimedia Pivot

14 Nov 2016 Hideki Nakayama Noriki Nishida

We propose an approach to build a neural machine translation system with no supervised resources (i.e., no parallel corpora) using multimodal embedded representation over texts and images. Based on the assumption that text documents are often likely to be described with other multimedia information (e.g., images) somewhat related to the content, we try to indirectly estimate the relevance between two languages... (read more)

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