  Controllable text generation is an appealing but challenging task, which
allows users to specify particular attributes of the generated outputs. In this
paper, we propose a controllable dialogue generation model to steer response
generation under multi-attribute constraints. Specifically, we define and
categorize the commonly used control attributes into global and local ones,
which possess different granularities of effects on response generation. Then,
we significantly extend the conventional seq2seq framework by introducing a
novel two-stage decoder, which first uses a multi-grained style specification
layer to impose the stylistic constraints and determine word-level control
states of responses based on the attributes, and then employs a response
generation layer to generate final responses maintaining both semantic
relevancy to the contexts and fidelity to the attributes. Furthermore, we train
our model with an attribute consistency reward to promote response control with
explicit supervision signals. Extensive experiments and in-depth analyses on
two datasets indicate that our model can significantly outperform competitive
baselines in terms of response quality, content diversity and controllability.
