  Recent work has demonstrated that vector offsets obtained by subtracting
pretrained word embedding vectors can be used to predict lexical relations with
surprising accuracy. Inspired by this finding, in this paper, we extend the
idea to the document level, in generating document-level embeddings,
calculating the distance between them, and using a linear classifier to
classify the relation between the documents. In the context of duplicate
detection and dialogue act tagging tasks, we show that document-level
difference vectors have utility in assessing document-level similarity, but
perform less well in multi-relational classification.
