This is a really interesting example. I can't read Chinese but I'm assuming the input is an address or location? Such domain knowledge would be vital in provid

This is a really interesting example. I can't read Chinese but I'm assuming the input is an address or location? Such domain knowledge would be vital in providing a good translation and sanity checking the output. But a straight sequence to sequence machine translation would not capture that context. It looks like thay is what is happening with the first two translations, while Google's may have actually realized its and address (but maybe not as you say the answer is wrong, maybe their ML is just better). Your example highlights the point that naked ML models can only ever be so good, and that it's really as part of a system that they can be truly effective. You can imagine in the translation some combination of a classifier or NER that identifies an address, a translation model, and an english model that detects a sensible answer.