  Recurrent and convolutional neural networks comprise two distinct families of
models that have proven to be useful for encoding natural language utterances.
In this paper we present SoPa, a new model that aims to bridge these two
approaches. SoPa combines neural representation learning with weighted
finite-state automata (WFSAs) to learn a soft version of traditional surface
patterns. We show that SoPa is an extension of a one-layer CNN, and that such
CNNs are equivalent to a restricted version of SoPa, and accordingly, to a
restricted form of WFSA. Empirically, on three text classification tasks, SoPa
is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline,
and is particularly useful in small data settings.
