  In a conversation or a dialogue process, attention and intention play
intrinsic roles. This paper proposes a neural network based approach that
models the attention and intention processes. It essentially consists of three
recurrent networks. The encoder network is a word-level model representing
source side sentences. The intention network is a recurrent network that models
the dynamics of the intention process. The decoder network is a recurrent
network produces responses to the input from the source side. It is a language
model that is dependent on the intention and has an attention mechanism to
attend to particular source side words, when predicting a symbol in the
response. The model is trained end-to-end without labeling data. Experiments
show that this model generates natural responses to user inputs.
