Follow-on Question Suggestion via Voice Hints for Voice Assistants
Besnik Fetahu Pedro Faustini Giuseppe Castellucci Anjie Fang Oleg Rokhlenko Shervin Malmasi
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.