Latent Variable Model for Multi-modal Translation

Miguel Angel Rios Gaona

Abstract:

In this work, we propose to model the interaction between visual and textual features
for multi-modal neural machine translation
(MMT) through a latent variable model. This
latent variable can be seen as a multi-modal
stochastic embedding of an image and its description in a foreign language. It is used
in a target-language decoder and also to predict image features. Importantly, our model
formulation utilises visual and textual inputs
during training but does not require that images be available at test time. We show
that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliottand Kad´ ar´ , 2017) and a conditional variational
auto-encoder approach (Toyama et al., 2016).
Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint
on the minimum amount of information encoded in the latent variable, and (iii) by training on additional target-language image descriptions (i.e. synthetic data).