  In this paper, we explore different neural network architectures that can
predict if a speaker of a given utterance is asking a question or making a
statement. We com- pare the outcomes of regularization methods that are
popularly used to train deep neural networks and study how different context
functions can affect the classification performance. We also compare the
efficacy of gated activation functions that are favorably used in recurrent
neural networks and study how to combine multimodal inputs. We evaluate our
models on two multimodal datasets: MSR-Skype and CALLHOME.
