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Recent work has demonstrated that vector offsets obtained by subtractingpretrained word embedding vectors can be used to predict lexical relations withsurprising accuracy. Inspired by this finding, in this paper, we extend theidea to the document level, in generating document-level embeddings,calculating the distance between them, and using a linear classifier toclassify the relation between the documents. In the context of duplicatedetection and dialogue act tagging tasks, we show that document-leveldifference vectors have utility in assessing document-level similarity, butperform less well in multi-relational classification.

Evaluating the Utility of Document Embedding Vector Difference for Relation Learning