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We propose an approach to build a neural machine translation system with nosupervised resources (i.e., no parallel corpora) using multimodal embeddedrepresentation over texts and images. Based on the assumption that textdocuments are often likely to be described with other multimedia information(e.g., images) somewhat related to the content, we try to indirectly estimatethe relevance between two languages. Using multimedia as the "pivot", weproject all modalities into one common hidden space where samples belonging tosimilar semantic concepts should come close to each other, whatever theobserved space of each sample is. This modality-agnostic representation is thekey to bridging the gap between different modalities. Putting a decoder on topof it, our network can flexibly draw the outputs from any input modality.Notably, in the testing phase, we need only source language texts as the inputfor translation. In experiments, we tested our method on two benchmarks to showthat it can achieve reasonable translation performance. We compared andinvestigated several possible implementations and found that an end-to-endmodel that simultaneously optimized both rank loss in multimodal encoders andcross-entropy loss in decoders performed the best.

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