Second, as demonstrated in the Redwood Lingo Treebank, reversibility makes it easy to rapidly create very large evaluation suites: it suffices to parse a set of sentences and select from the parser output the correct semantics.
nLG geared realisers either work on evaluation sets of restricted size (500 input for SURGE, 210 for KPML) or require the time expensive implementation of a preprocessor transforming e.g., Penn Treebank trees into a format suitable for the realisers.