  Nowadays, with many e-commerce platforms conducting global business,
e-commerce search systems are required to handle product retrieval under
multilingual scenarios. Moreover, comparing with maintaining per-country
specific e-commerce search systems, having a universal system across countries
can further reduce the operational and computational costs, and facilitate
business expansion to new countries. In this paper, we introduce a universal
end-to-end multilingual retrieval system, and discuss our learnings and
technical details when training and deploying the system to serve billion-scale
product retrieval for e-commerce search. In particular, we propose a
multilingual graph attention based retrieval network by leveraging recent
advances in transformer-based multilingual language models and graph neural
network architectures to capture the interactions between search queries and
items in e-commerce search. Offline experiments on five countries data show
that our algorithm outperforms the state-of-the-art baselines by 35% recall and
25% mAP on average. Moreover, the proposed model shows significant increase of
conversion/revenue in online A/B experiments and has been deployed in
production for multiple countries.
