  We conduct two experiments to study the effect of context on metaphor
paraphrase aptness judgments. The first is an AMT crowd source task in which
speakers rank metaphor paraphrase candidate sentence pairs in short document
contexts for paraphrase aptness. In the second we train a composite DNN to
predict these human judgments, first in binary classifier mode, and then as
gradient ratings. We found that for both mean human judgments and our DNN's
predictions, adding document context compresses the aptness scores towards the
center of the scale, raising low out of context ratings and decreasing high out
of context scores. We offer a provisional explanation for this compression
effect.
