We have presented a novel framework for learning a tunable MT evaluation metric in a pairwise ranking setting, given pre-existing pairwise human preference judgments.
we used a neural network, where the input layer encodes lexical, syntactic and semantic information from the reference and the two translation hypotheses, which is efficiently compacted into relatively small embeddings.