  Open domain dialog systems face the challenge of being repetitive and
producing generic responses. In this paper, we demonstrate that by conditioning
the response generation on interpretable discrete dialog attributes and
composed attributes, it helps improve the model perplexity and results in
diverse and interesting non-redundant responses. We propose to formulate the
dialog attribute prediction as a reinforcement learning (RL) problem and use
policy gradients methods to optimize utterance generation using long-term
rewards. Unlike existing RL approaches which formulate the token prediction as
a policy, our method reduces the complexity of the policy optimization by
limiting the action space to dialog attributes, thereby making the policy
optimization more practical and sample efficient. We demonstrate this with
experimental and human evaluations.
