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Personal assistant AI systems such as Siri, Cortana, and Alexa have becomewidely used as a means to accomplish tasks through natural language commands.However, components in these systems generally rely on supervised machinelearning algorithms that require large amounts of hand-annotated training data,which is expensive and time consuming to collect. The ability to incorporateunsupervised, weakly supervised, or distantly supervised data holds significantpromise in overcoming this bottleneck. In this paper, we describe a frameworkthat leverages user engagement signals (user behaviors that demonstrate apositive or negative response to content) to automatically create granularentity labels for training data augmentation. Strategies such as multi-tasklearning and validation using an external knowledge base are employed toincorporate the engagement annotated data and to boost the model's accuracy ona sequence labeling task. Our results show that learning from dataautomatically labeled by user engagement signals achieves significant accuracygains in a production deep learning system, when measured on both the sequencelabeling task as well as on user facing results produced by the systemend-to-end. We believe this is the first use of user engagement signals to helpgenerate training data for a sequence labeling task on a large scale, and canbe applied in practical settings to speed up new feature deployment when littlehuman annotated data is available.

Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant