  Recurrent neural networks such as the GRU and LSTM found wide adoption in
natural language processing and achieve state-of-the-art results for many
tasks. These models are characterized by a memory state that can be written to
and read from by applying gated composition operations to the current input and
the previous state. However, they only cover a small subset of potentially
useful compositions. We propose Multi-Function Recurrent Units (MuFuRUs) that
allow for arbitrary differentiable functions as composition operations.
Furthermore, MuFuRUs allow for an input- and state-dependent choice of these
composition operations that is learned. Our experiments demonstrate that the
additional functionality helps in different sequence modeling tasks, including
the evaluation of propositional logic formulae, language modeling and sentiment
analysis.
