The most popular way of finding a translation for a source sentence with a neural sequence-to-sequence model is a simple beam search. The target sentence is predicted one word at a time and after each prediction, a fixed number of possibilities (typically between 4 and 10) is retained for further exploration. This strategy can be suboptimal as these local hard decisions do not take the remainder of the translation into account and can not be reverted later on.
Alessandra Binazzi has seen what strong localization programs can accomplish and knows what it takes to get there. After all, as the Head of Global Localization at ASICS Digital, she understands how to grow a global brand and expand into new locales and regions.