Although attention-based Neural Machine Translation have achieved great success, attention-mechanism cannot capture the entire meaning of the source sentence because the attention mechanism generates a target word depending heavily on the relevant parts of the source sentence. The report of earlier studies has introduced a latent variable to capture the entire meaning of sentence and achieved improvement on attention-based Neural Machine Translation. We follow this approach and we believe that the capturing meaning of sentence benefits from image information because human beings understand the meaning of language not only from textual information but also from perceptual information such as that gained from vision. As described herein, we propose a neural machine translation model that introduces a continuous latent variable containing an underlying semantic extracted from texts and images. Our model, which can be trained endto-end, requires image information only when training. Experiments conducted with an English-German translation task show that our model outperforms over the baseline.
REF extend the Variational Neural Machine Translation model by inferring latent variables to explicitly model the semantics of source sentences from both image and linguistic information.
Neural Machine Translation with Latent Semantic of Image and Text
ArXiv
ArXiv
Computer Science