Hmm... The authors literally ran a series of experiments and published them without giving a clear way to reproduce the results. How is that useful to anyone except to self advertise?
My comment about prediction vs understanding was simply meant to underscore this, albeit it might rub some people the wrong way. If you publish a paper purely about prediction (ie a set of experiments) be prepared to release all pertinent information to reproduce said experiments. If you choose to publish a paper that aims to improve mankind's understanding of the problem at hand, you are intrinsically required to provide all proof in your exposition.
Otherwise we might as well just believe everything anyone ever says with no proof.
It beats existing commercial products that try to do the same, so to me it seems it has some value right now.
While I agree with your conclusion, a minor correction is in order here: it beats a (as in a single selected) commercial product and a single free OSS transpiler.
The improvement for the commercial product is 61% vs. 75% accuracy (i.e. 23% better) which - while impressive given the unsupervised learning aspect - isn't a game changer (yet!).