  Translating e-commercial product descriptions, a.k.a product-oriented machine
translation (PMT), is essential to serve e-shoppers all over the world.
However, due to the domain specialty, the PMT task is more challenging than
traditional machine translation problems. Firstly, there are many specialized
jargons in the product description, which are ambiguous to translate without
the product image. Secondly, product descriptions are related to the image in
more complicated ways than standard image descriptions, involving various
visual aspects such as objects, shapes, colors or even subjective styles.
Moreover, existing PMT datasets are small in scale to support the research. In
this paper, we first construct a large-scale bilingual product description
dataset called Fashion-MMT, which contains over 114k noisy and 40k manually
cleaned description translations with multiple product images. To effectively
learn semantic alignments among product images and bilingual texts in
translation, we design a unified product-oriented cross-modal cross-lingual
model (\upoc~) for pre-training and fine-tuning. Experiments on the Fashion-MMT
and Multi30k datasets show that our model significantly outperforms the
state-of-the-art models even pre-trained on the same dataset. It is also shown
to benefit more from large-scale noisy data to improve the translation quality.
We will release the dataset and codes at
https://github.com/syuqings/Fashion-MMT.
