Indeed, translating from the #1 language to the #4 language (or back) is problematic. Google's translation is usually enough to approximate what was said, but o

Indeed, translating from the #1 language to the #4 language (or back) is problematic. Google's translation is usually enough to approximate what was said, but often results in gibberish. As a small example, it consistently fails with the spanish pronoun "su" - which is a word that depending on context can mean "his", "hers", "its", "your", "y'all's", "their" -- which seems to be a good example of the compressibility of natural language. How big does the corpus used to train their models need to be to get "su" right?