  We present the multiplicative recurrent neural network as a general model for
compositional meaning in language, and evaluate it on the task of fine-grained
sentiment analysis. We establish a connection to the previously investigated
matrix-space models for compositionality, and show they are special cases of
the multiplicative recurrent net. Our experiments show that these models
perform comparably or better than Elman-type additive recurrent neural networks
and outperform matrix-space models on a standard fine-grained sentiment
analysis corpus. Furthermore, they yield comparable results to structural deep
models on the recently published Stanford Sentiment Treebank without the need
for generating parse trees.
