  In this paper we focus on the problem of dialog act (DA) labelling. This
problem has recently attracted a lot of attention as it is an important
sub-part of an automatic question answering system, which is currently in great
demand. Traditional methods tend to see this problem as a sequence labelling
task and deals with it by applying classifiers with rich features. Most of the
current neural network models still omit the sequential information in the
conversation. Henceforth, we apply a novel multi-level gated recurrent neural
network (GRNN) with non-textual information to predict the DA tag. Our model
not only utilizes textual information, but also makes use of non-textual and
contextual information. In comparison, our model has shown significant
improvement over previous works on Switchboard Dialog Act (SWDA) task by over
6%.
