You could use the same argument against pretty much any discipline that's undergoing active research. Of course no-one knows (yet) how to do it properly or else there would be no research going on. Image understanding, robotics, even non-computational disciplines such as medicine... Staying with the latter, take HIV for example: no-one knows how to heal it but I'm sure a lot of people are very grateful for the 80% solutions that prolong lives today.

So, in summary, you point is not wrong. But it's no reason for bashing computational linguistics. It is common across many disciplines to use not-yet-perfect solutions as long as you don't know how to do better.

That said, I don't fully agree with the notion that "hacking a solution" is the suggested way of doing things. Computational linguistics is a pretty wild field with a lot of sub-disciplines. In a lot of those, the state of the art consists of quite sophisticated approaches that are the result of years of research. Take speech recognition, for instance. Currently, deep learning approaches take the cake, but there is also a plethora of insights that have been gained from improving the traditional methods over decades.

I think, a more nuanced point of view is called for here.