  The compositionality of meaning extends beyond the single sentence. Just as
words combine to form the meaning of sentences, so do sentences combine to form
the meaning of paragraphs, dialogues and general discourse. We introduce both a
sentence model and a discourse model corresponding to the two levels of
compositionality. The sentence model adopts convolution as the central
operation for composing semantic vectors and is based on a novel hierarchical
convolutional neural network. The discourse model extends the sentence model
and is based on a recurrent neural network that is conditioned in a novel way
both on the current sentence and on the current speaker. The discourse model is
able to capture both the sequentiality of sentences and the interaction between
different speakers. Without feature engineering or pretraining and with simple
greedy decoding, the discourse model coupled to the sentence model obtains
state of the art performance on a dialogue act classification experiment.
