That's actually a good point and one I've made myself a few times in the past- negative results never see the light of day. So, yes, I agree we can't know for sure that such systems haven't been attempted, despite the certainty with which I assert this in my comment above.
On the other hand, there are some strong hints. First of all, there was an AI winter in the 1980's after which most of the field turned very hard towards statistical techniques and away from the kind of work in SHRDLU. This kind of work became radioactive for many years and it would have been very hard to justify putting a PhD student, or ten, to work trying to even reproduce it, let alone extend it. That's in academia. In the industry, it's clear that nowadays at least companies like the FANGs strongly champion statistical machine learning and anyone proposing spending actual money on such a research program ("Hey, let's go back to the 1960's and start all over again!") would be laughed out of the building. That is, I believe there are strong hints that the political climate in academia and the culture in large companies, has suppressed any attempt to do work of this kind. But that's only my conjecture, so there you have it.
>> It is inaccurate to say that all benchmarks are useless.
Of course. My point is that current benchmarks are useless.
The fact that you find Google translate useful is unrelated to how well Google translate scores in benchmarks, which are not designed to measure user satisfaction but instead are supposed to tell us something about the formal properties of the system. In any case, for translation in particular, it's not controversial that there are no good benchmarks and metrics and many people in NLP will tell you that is the case. In fact, I'm saying this myself because I was told during my Master's, by our NLP tutor who is a researcher in the field. Also see the following article which indcludes a discussion of commonly used metrics in machine translation and the difficulty of evaluating machine translation systems: