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An intelligent dialogue system in a multi-turn setting should not onlygenerate the responses which are of good quality, but it should also generatethe responses which can lead to long-term success of the dialogue. Although,the current approaches improved the response quality, but they over-look thetraining signals present in the dialogue data. We can leverage these signals togenerate the weakly supervised training data for learning dialog policy andreward estimator, and make the policy take actions (generates responses) whichcan foresee the future direction for a successful (rewarding) conversation. Wesimulate the dialogue between an agent and a user (modelled similar to an agentwith supervised learning objective) to interact with each other. The agent usesdynamic blocking to generate ranked diverse responses andexploration-exploitation to select among the Top-K responses. Each simulatedstate-action pair is evaluated (works as a weak annotation) with three qualitymodules: Semantic Relevant, Semantic Coherence and Consistent Flow. Empiricalstudies with two benchmarks indicate that our model can significantlyout-perform the response quality and lead to a successful conversation on bothautomatic evaluation and human judgement.

WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue