We hypothesize that if a dataset uses a wide variety of words to express the same ideas, similarity metrics that rely on the surface form of the sentence will be at a disadvantage compared to similarity metrics based in distributional semantics.
word2vec, LSTM autoencoders, and skip-thoughts ought to perform better than the n-gram-based methods and dependency count method when applied to CLUE, but should enjoy no advantage when applied to PAS.