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In the context of a voice assistant system, steering refers to the phenomenonin which a user issues a follow-up command attempting to direct or clarify aprevious turn. We propose STEER, a steering detection model that predictswhether a follow-up turn is a user's attempt to steer the previous command.Constructing a training dataset for steering use cases poses challenges due tothe cold-start problem. To overcome this, we developed heuristic rules tosample opt-in usage data, approximating positive and negative samples withoutany annotation. Our experimental results show promising performance inidentifying steering intent, with over 95% accuracy on our sampled data.Moreover, STEER, in conjunction with our sampling strategy, aligns effectivelywith real-world steering scenarios, as evidenced by its strong zero-shotperformance on a human-graded evaluation set. In addition to relying solely onuser transcripts as input, we introduce STEER+, an enhanced version of themodel. STEER+ utilizes a semantic parse tree to provide more context onout-of-vocabulary words, such as named entities that often occur at thesentence boundary. This further improves model performance, reducing error ratein domains where entities frequently appear, such as messaging. Lastly, wepresent a data analysis that highlights the improvement in user experience whenvoice assistants support steering use cases.

STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants