  Document editing has become a pervasive component of the production of
information, with version control systems enabling edits to be efficiently
stored and applied. In light of this, the task of learning distributed
representations of edits has been recently proposed. With this in mind, we
propose a novel approach that employs variational inference to learn a
continuous latent space of vector representations to capture the underlying
semantic information with regard to the document editing process. We achieve
this by introducing a latent variable to explicitly model the aforementioned
features. This latent variable is then combined with a document representation
to guide the generation of an edited version of this document. Additionally, to
facilitate standardized automatic evaluation of edit representations, which has
heavily relied on direct human input thus far, we also propose a suite of
downstream tasks, PEER, specifically designed to measure the quality of edit
representations in the context of natural language processing.
