  In this work, we investigate a more realistic unsupervised multimodal machine
translation (UMMT) setup, inference-time image-free UMMT, where the model is
trained with source-text image pairs, and tested with only source-text inputs.
First, we represent the input images and texts with the visual and language
scene graphs (SG), where such fine-grained vision-language features ensure a
holistic understanding of the semantics. To enable pure-text input during
inference, we devise a visual scene hallucination mechanism that dynamically
generates pseudo visual SG from the given textual SG. Several SG-pivoting based
learning objectives are introduced for unsupervised translation training. On
the benchmark Multi30K data, our SG-based method outperforms the
best-performing baseline by significant BLEU scores on the task and setup,
helping yield translations with better completeness, relevance and fluency
without relying on paired images. Further in-depth analyses reveal how our
model advances in the task setting.
