I'm going to say this is probably state of the art among methods that learn from scratch from a corpus of text. I say "probably" because they skipped most of the usual word-similarity evaluations, only using RW.

The RW results are still lower than any results for ConceptNet Numberbatch [1] in the case where you are able to use a knowledge graph. This is still an advance -- from my point of view, these vectors are an improved input for Numberbatch -- but it continues to surprise me how the possibility of learning from a knowledge graph is not even mentioned when the big players write about these evaluations.

It also pains me that the only analogies evaluated are Mikolov et al. (2013), which is a huge huge case of Not Invented Here just because Mikolov is the first author. It is not a good evaluation [2]. It is the same 10 or so boring analogies over and over. I would much rather see BATS [3] or SemEval-2012, or even Turney's set of real SAT analogies despite their non-free status. But this would require Mikolov to admit that he did not come up with the perfect semantic evaluation off the top of his head, on his first try, without referring to any of the work already done by Turney and others.

[1] https://github.com/commonsense/conceptnet-numberbatch

[2] https://www.aclweb.org/anthology/N/N16/N16-2002.pdf

[3] https://aclweb.org/aclwiki/Bigger_analogy_test_set_(State_of...