  In the era of deep learning, word embeddings are essential when dealing with
text tasks. However, storing and accessing these embeddings requires a large
amount of space. This is not conducive to the deployment of these models on
resource-limited devices. Combining the powerful compression capability of
tensor products, we propose a word embedding compression method with
morphological augmentation, Morphologically-enhanced Tensorized Embeddings
(MorphTE). A word consists of one or more morphemes, the smallest units that
bear meaning or have a grammatical function. MorphTE represents a word
embedding as an entangled form of its morpheme vectors via the tensor product,
which injects prior semantic and grammatical knowledge into the learning of
embeddings. Furthermore, the dimensionality of the morpheme vector and the
number of morphemes are much smaller than those of words, which greatly reduces
the parameters of the word embeddings. We conduct experiments on tasks such as
machine translation and question answering. Experimental results on four
translation datasets of different languages show that MorphTE can compress word
embedding parameters by about 20 times without performance loss and
significantly outperforms related embedding compression methods.
