Just having the state of the art as open source is in itself fantastic. The fact that their approach is a considerable improvement over the previous approaches is icing on the cake.
Actually, for pretty much any NLP problem the state of art is open source - they often aren't packaged as convenient libraries, but the actual best-in-field methods usually have both detailed algorithm descriptions in the published papers (from which we can and sometimes do a direct reimplementation), and a reference implementation with available source, that they used to get the measurements proving that it really is state of the art. Sure, those research implementations tend to be 'not-production' level of polish, often needing some pain to install and convert your particular data; but they are available. In a few cases the best known method is a commercial implementation; but then usually the #2 implementation is almost as good and that's available.
