  The adoption of voice assistants like Alexa or Siri has grown rapidly,
allowing users to instantly access information via voice search. Query
suggestion is a standard feature of screen-based search experiences, allowing
users to explore additional topics. However, this is not trivial to implement
in voice-based settings. To enable this, we tackle the novel task of suggesting
questions with compact and natural voice hints to allow users to ask follow-up
questions.
  We define the task, ground it in syntactic theory and outline linguistic
desiderata for spoken hints. We propose baselines and an approach using
sequence-to-sequence Transformers to generate spoken hints from a list of
questions. Using a new dataset of 6681 input questions and human written hints,
we evaluated the models with automatic metrics and human evaluation. Results
show that a naive approach of concatenating suggested questions creates poor
voice hints. Our approach, which applies a linguistically-motivated pretraining
task was strongly preferred by humans for producing the most natural hints.

Computation and Language