The cognitive scientist Douglas R. Hofstadter has argued for many years that effective translation of any but the most pedestrian and constrained texts requires that the translator understand the text, its context, and its purpose. In this article, he reviews the current performance of Google Translate and concludes that massive text databases and machine-learning algorithms don't simulate this understanding well.
“The Shallowness of Google Translate”
Douglas Hofstadter, The Atlantic, January 30, 2018
In Language Log, a professional Sinologist does a slightly deeper dive into one of Hofstadter's examples and suggests that Hofstadter is attacking a straw man.
“Don't Blame Google Translate”
Victor Mair, Language Log, February 4, 2018
It is easy to find inadequacies in the GT translations, and Hofstadter does so systematically, but I don't think anyone in their right mind would expect a machine to do as good a job as a skilled, experienced, sensitive, creative human translator who knows both the source language and the target language well.
If GT and other machine translators are unable to do a perfect job, or even one that is close to what a skilled human translator is capable of, what are their purposes? I believe that they fulfill a useful function in giving us the gist of meaning of texts written in languages with which we are unfamiliar.
In defense of Hofstadter, I note that what prompted his investigation was his discovery that two of his friends, each fluent in the other's native language, nevertheless used Google Translate as an intermediary when corresponding by e-mail: They each wrote in their native language, fed the result through Google Translate, and sent off the result.
How odd! Why would two intelligent people, each of whom spoke the other's language well, do this? My own experiences with machine-translation software had always led me to be highly skeptical about it. But my skepticism was clearly not shared by these two. Indeed, many thoughtful people are quite enamored of translation programs, finding little to criticize in them. This baffles me.
All of his examples are cases in which the machine-translation system has failed to render the main point of the passage, what Mair calls “the gist,” because it doesn't understand the passage. They are cases in which the system fails even at the minimal “useful function” that Mair identifies.