  Some exciting new approaches to neural architectures for the analysis of
conversation have been introduced over the past couple of years. These include
neural architectures for detecting emotion, dialogue acts, and sentiment
polarity. They take advantage of some of the key attributes of contemporary
machine learning, such as recurrent neural networks with attention mechanisms
and transformer-based approaches. However, while the architectures themselves
are extremely promising, the phenomena they have been applied to to date are
but a small part of what makes conversation engaging. In this paper we survey
these neural architectures and what they have been applied to. On the basis of
the social science literature, we then describe what we believe to be the most
fundamental and definitional feature of conversation, which is its
co-construction over time by two or more interlocutors. We discuss how neural
architectures of the sort surveyed could profitably be applied to these more
fundamental aspects of conversation, and what this buys us in terms of a better
analysis of conversation and even, in the longer term, a better way of
generating conversation for a conversational system.
