STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants

In the context of voice assistant systems, steering is when users issue follow-up commands to direct or clarify previous turns. We propose STEER, a model that detects whether a follow-up turn is the user attempting to steer the previous command. To train the model, we created heuristic rules to sample usage data without annotations, overcoming the cold-start problem. Experimental results are provided.

 STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants

Introducing STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants

In the world of voice assistants, steering refers to when a user gives a follow-up command to clarify or direct a previous instruction. To address this, we have developed STEER, a steering detection model that predicts whether a follow-up turn is an attempt to steer the previous command. Our model overcomes the challenge of constructing a training dataset for steering use cases by using heuristic rules to sample opt-in usage data, without any annotation. Our experimental results…

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