  Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to
its strong capabilities to encode word content, CBOW embeddings perform well on
a wide range of downstream tasks while being efficient to compute. However,
CBOW is not capable of capturing the word order. The reason is that the
computation of CBOW's word embeddings is commutative, i.e., embeddings of XYZ
and ZYX are the same. In order to address this shortcoming, we propose a
learning algorithm for the Continuous Matrix Space Model, which we call
Continual Multiplication of Words (CMOW). Our algorithm is an adaptation of
word2vec, so that it can be trained on large quantities of unlabeled text. We
empirically show that CMOW better captures linguistic properties, but it is
inferior to CBOW in memorizing word content. Motivated by these findings, we
propose a hybrid model that combines the strengths of CBOW and CMOW. Our
results show that the hybrid CBOW-CMOW-model retains CBOW's strong ability to
memorize word content while at the same time substantially improving its
ability to encode other linguistic information by 8%. As a result, the hybrid
also performs better on 8 out of 11 supervised downstream tasks with an average
improvement of 1.2%.
