  We introduce a class of convolutional neural networks (CNNs) that utilize
recurrent neural networks (RNNs) as convolution filters. A convolution filter
is typically implemented as a linear affine transformation followed by a
non-linear function, which fails to account for language compositionality. As a
result, it limits the use of high-order filters that are often warranted for
natural language processing tasks. In this work, we model convolution filters
with RNNs that naturally capture compositionality and long-term dependencies in
language. We show that simple CNN architectures equipped with recurrent neural
filters (RNFs) achieve results that are on par with the best published ones on
the Stanford Sentiment Treebank and two answer sentence selection datasets.
