  An intelligent dialogue system in a multi-turn setting should not only
generate the responses which are of good quality, but it should also generate
the responses which can lead to long-term success of the dialogue. Although,
the current approaches improved the response quality, but they over-look the
training signals present in the dialogue data. We can leverage these signals to
generate the weakly supervised training data for learning dialog policy and
reward estimator, and make the policy take actions (generates responses) which
can foresee the future direction for a successful (rewarding) conversation. We
simulate the dialogue between an agent and a user (modelled similar to an agent
with supervised learning objective) to interact with each other. The agent uses
dynamic blocking to generate ranked diverse responses and
exploration-exploitation to select among the Top-K responses. Each simulated
state-action pair is evaluated (works as a weak annotation) with three quality
modules: Semantic Relevant, Semantic Coherence and Consistent Flow. Empirical
studies with two benchmarks indicate that our model can significantly
out-perform the response quality and lead to a successful conversation on both
automatic evaluation and human judgement.
