  In the context of a voice assistant system, steering refers to the phenomenon
in which a user issues a follow-up command attempting to direct or clarify a
previous turn. We propose STEER, a steering detection model that predicts
whether 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 to
the cold-start problem. To overcome this, we developed heuristic rules to
sample opt-in usage data, approximating positive and negative samples without
any annotation. Our experimental results show promising performance in
identifying steering intent, with over 95% accuracy on our sampled data.
Moreover, STEER, in conjunction with our sampling strategy, aligns effectively
with real-world steering scenarios, as evidenced by its strong zero-shot
performance on a human-graded evaluation set. In addition to relying solely on
user transcripts as input, we introduce STEER+, an enhanced version of the
model. STEER+ utilizes a semantic parse tree to provide more context on
out-of-vocabulary words, such as named entities that often occur at the
sentence boundary. This further improves model performance, reducing error rate
in domains where entities frequently appear, such as messaging. Lastly, we
present a data analysis that highlights the improvement in user experience when
voice assistants support steering use cases.
