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.
Without context it's a good attempt, only with deeper cultural knowledge would someone be able to guess that "Miao" on its own is too short for a street name and it's likely "Tangmiao" together, though without knowing the place I think it would also be reasonable to guess that the whole thing is the road name like "Gudangtangmiao Road".
Actually DeepL seems to be the best of the bunch.
Here is another test:
Input: 北京市西城区地安门西大街49号
DeepL: No.49, Di'anmen West Street, Xicheng District, Beijing
Google: 49 Di'anmen West Street, Xicheng District, Beijing
Watson: No. 49 Avenue West Main Street in Xicheng District, Beijing
Libre: 49th Anniversary Street, Westtown, Beijing
(Libre is the worst for all address type input - which is what I'm interested in - shameless plug - I'm building a Geocoder from China at https://geocode.xyz/CN . I've so far tested over 3k addresses)
Deepl seems to have a bit of an issue with uncommon street suffixes in Chinese. For example:
江苏省苏州市姑苏区东中市374号槔桥头
Google: Bridge Head, No. 374, Dongzhong City, Gusu District, Suzhou City, Jiangsu Province
Deepl: Pulley Bridge, No. 374, Dongzhong City, Gusu District, Suzhou, Jiangsu Province
Libre: Cambridge No. 374 in the eastern part of the city of Jiangsu, province of Jiangsu
Watson: No. 374 Bridge Head in East China, Suzhou, Suzhou, Jiangsu Province.
You can kind of see which service goes for the literal more than the interpretive. It might be a bit unfair to use an address that is more than just the literal street address, although in actual speech, this address is at an intersection with two bridges and it's enough of a local landmark that the addition would make sense. Except for the uncertainty over the proper name of the bridge that actually doesn't throw Google or Deepl off. It's the 市suffix for a street, not unique in this city but certainly rare, that gets all of the services. Libre at least gives it a try, Watson just pretends like it doesn't exist and for whatever reason translates the old name of the city to the new name of the city, which obviously now encompasses a much greater area and exists in a different context. Deepl seems to have figured out that you aren't really supposed to have two cities in one address and tries to rectify that in spite of the literal. I would imagine that a human translator would use the entirety of the street name and add "street" in English to the end. Definitely interesting to see how these services handle somewhat nonstandard and much older address patterns that don't originate necessarily in Mandarin and frequently relates to local landmarks that no longer exist, all of which requires some contextual work beyond your standaard post-1949 naming of streets that tends to be fairly standardized both thematically and in form.
Chinese Input: 古荡塘苗路华星现代产业园E座正门
LibreTranslate: Ordinary gate of the modern industrial plant of the Hyong Chung Chung
IBM Watson: Ancient Slut Pond Miao Luhua Star Modern Industrial Park E Zhengmen
Google Translate: Main entrance of Block E, Huaxing Modern Industrial Park, Miao Road, Gudangtang
None is accurate, but it is nice to have options.