Self-learning paradigms in large-scale conver- sational AI agents tend to leverage user feed- back in bridging between what they say and what they mean. However, such learning, par- ticularly in Markov-based query rewriting sys- tems have far from addressed the impact of these models on future training where succes- sive feedback is inevitably contingent on the rewrite itself, especially in a continually up- dating environment. In this paper, we explore the consequences of this inherent lack of self- awareness towards impairing the model perfor- mance, ultimately resulting in both Type I and II errors over time. To that end, we propose aug- menting the Markov Graph construction with a superposition-based adjacency matrix. Here, our method leverages an induced stochastic- ity to reactively learn a locally-adaptive deci- sion boundary based on the performance of the individual rewrites in a bi-variate beta set- ting. We also surface a data augmentation strat- egy that leverages template-based generation in abridging complex conversation hierarchies of dialogs so as to simplify the learning pro- cess. 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.

Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI

5F: Industry Oral Session 2

technical paper

## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Clint Solomon Mathialagan

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Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI

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