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We have been witnessing the usefulness of conversational AI systems such asSiri and Alexa, directly impacting our daily lives. These systems normally relyon machine learning models evolving over time to provide quality userexperience. However, the development and improvement of the models arechallenging because they need to support both high (head) and low (tail) usagescenarios, requiring fine-grained modeling strategies for specific data subsetsor slices. In this paper, we explore the recent concept of slice-based learning(SBL) (Chen et al., 2019) to improve our baseline conversational skill routingsystem on the tail yet critical query traffic. We first define a set oflabeling functions to generate weak supervision data for the tail intents. Wethen extend the baseline model towards a slice-aware architecture, whichmonitors and improves the model performance on the selected tail intents.Applied to de-identified live traffic from a commercial conversational AIsystem, our experiments show that the slice-aware model is beneficial inimproving model performance for the tail intents while maintaining the overallperformance.

Handling Long-Tail Queries with Slice-Aware Conversational Systems