  We present an efficient document representation learning framework, Document
Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a
simple average of word embeddings. It ensures a representation generated as
such captures the semantic meanings of the document during learning. A
corruption model is included, which introduces a data-dependent regularization
that favors informative or rare words while forcing the embeddings of common
and non-discriminative ones to be close to zero. Doc2VecC produces
significantly better word embeddings than Word2Vec. We compare Doc2VecC with
several state-of-the-art document representation learning algorithms. The
simple model architecture introduced by Doc2VecC matches or out-performs the
state-of-the-art in generating high-quality document representations for
sentiment analysis, document classification as well as semantic relatedness
tasks. The simplicity of the model enables training on billions of words per
hour on a single machine. At the same time, the model is very efficient in
generating representations of unseen documents at test time.
