  Self-learning paradigms in large-scale conversational AI agents tend to
leverage user feedback in bridging between what they say and what they mean.
However, such learning, particularly in Markov-based query rewriting systems
have far from addressed the impact of these models on future training where
successive feedback is inevitably contingent on the rewrite itself, especially
in a continually updating environment. In this paper, we explore the
consequences of this inherent lack of self-awareness towards impairing the
model performance, ultimately resulting in both Type I and II errors over time.
To that end, we propose augmenting the Markov Graph construction with a
superposition-based adjacency matrix. Here, our method leverages an induced
stochasticity to reactively learn a locally-adaptive decision boundary based on
the performance of the individual rewrites in a bi-variate beta setting. We
also surface a data augmentation strategy that leverages template-based
generation in abridging complex conversation hierarchies of dialogs so as to
simplify the learning process. All in all, we demonstrate that our self-aware
model improves the overall PR-AUC by 27.45%, achieves a relative defect
reduction of up to 31.22%, and is able to adapt quicker to changes in global
preferences across a large number of customers.
