We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives. These systems normally rely on machine learning models evolving over time to provide quality user experience. However, the development and improvement of the models are challenging because they need to support both high (head) and low (tail) usage scenarios, requiring fine-grained modeling strategies for specific data subsets or slices. In this paper, we explore the recent concept of slice-based learning (SBL) (Chen et al., 2019) to improve our baseline conversational skill routing system on the tail yet critical query traffic. We first define a set of labeling functions to generate weak supervision data for the tail intents. We then extend the baseline model towards a slice-aware architecture, which monitors and improves the model performance on the selected tail intents. Applied to de-identified live traffic from a commercial conversational AI system, our experiments show that the slice-aware model is beneficial in improving model performance for the tail intents while maintaining the overall performance.

本文探讨了基于切片学习的方法来提高基础对话路由系统的性能，通过定义一组标签函数来生成针对尾部意图的弱监督数据，并将基线模型延伸至切片感知的体系结构，以监视和提高对选定尾部意图的模型性能。应用于商业对话AI系统中的非身份识别实时数据，实验结果显示，切片感知模型有助于提高尾部意图的模型性能同时保持整体性能。

使用切片知识的对话系统应对长尾查询