  Ever since the successful application of sequence to sequence learning for
neural machine translation systems, interest has surged in its applicability
towards language generation in other problem domains. Recent work has
investigated the use of these neural architectures towards modeling open-domain
conversational dialogue, where it has been found that although these models are
capable of learning a good distributional language model, dialogue coherence is
still of concern. Unlike translation, conversation is much more a one-to-many
mapping from utterance to a response, and it is even more pressing that the
model be aware of the preceding flow of conversation. In this paper we propose
to tackle this problem by introducing previous conversational context in terms
of latent representations of dialogue acts over time. We inject the latent
context representations into a sequence to sequence neural network in the form
of dialog acts using a second encoder to enhance the quality and the coherence
of the conversations generated. The main task of this research work is to show
that adding latent variables that capture discourse relations does indeed
result in more coherent responses when compared to conventional sequence to
sequence models.
