The thing is, the value as a baseline doesn't actually change that much for being 20% garbage. A bit counter-intuitive, but basically accepted as true in several fields.

The comparisons are all relative accuracy, not absolute accuracy. And the comparison is fair. The new technique is receiving the same part-garbage input that the old-techniques were trained on. For the most part, the better technique will still tend to do better unless there's specifically something about it that makes it more sensitive to labeling errors.

And frankly, a percentage of junk has some advantages. Real-word data is a pile of ass, so it's useful for academic models to require robustness.