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.

在语音助手系统中，steering 是指用户发出后续指令以引导或澄清上一轮对话的现象。我们提出了一种名为 STEER 的 steering 检测模型，能够预测后续指令是否是用户试图引导上一个指令的意图。我们通过开发启发式规则来构建训练数据集，以应对 steering 用例的冷启动问题，并且在我们的采样数据上取得了超过 95% 的准确率。此外，我们引入了 STEER+，它在用户转录的基础上利用语义解析树来提供更多上下文信息，特别是对于通常出现在句子边界的命名实体等生词。我们还展示了一项数据分析，突出了语音助手支持 steering 用例时用户体验的改善。