Before revision:  In this paper, we hypothesize that visual imagination via synthesizing visual representation from source text could help the neural model map two languages with different symbols, thus helps the translation task. Our proposed end-to-end imagination-based machine translation model (ImagiT ) first learns to generate semantic-consistent visual representation from  source sentence, and then generate target sentence based on both text representation and imagined visual representation.
After revision:  In this paper, we propose ImagiT, a novel machine translation method via visual imagination. ImagiT first learns to generate semantic-consistent visual representation from source sentence, and then generate target sentence based on both text representation and imagined visual representation.