  We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural
network for efficient estimation of high-quality sentence embeddings. Averaging
the embeddings of words in a sentence has proven to be a surprisingly
successful and efficient way of obtaining sentence embeddings. However, word
embeddings trained with the methods currently available are not optimized for
the task of sentence representation, and, thus, likely to be suboptimal.
Siamese CBOW handles this problem by training word embeddings directly for the
purpose of being averaged. The underlying neural network learns word embeddings
by predicting, from a sentence representation, its surrounding sentences. We
show the robustness of the Siamese CBOW model by evaluating it on 20 datasets
stemming from a wide variety of sources.
