Paragraph vectors (Le and Mikolov, 2014 ) are a recent method for embedding pieces of natural language text as fixed-length, real-valued vectors. Extending the word2vec framework <cite>(Mikolov et al., 2013b)</cite> , paragraph vectors are typically presented as neural language models, and compute a single vector representation for each paragraph. Unlike word embeddings, paragraph vectors are not shared across the entire corpus, but are instead local to each paragraph. When interpreted as a latent variable, we expect them to have higher uncertainty when the paragraphs are short.