Spoken Question Answering (QA) is a key feature of voice assistants, usually
backed by multiple QA systems. Users ask questions via spontaneous speech which
can contain disfluencies, errors, and informal syntax or phrasing. This is a
major challenge in QA, causing unanswered questions or irrelevant answers, and
leading to bad user experiences. We analyze failed QA requests to identify core
challenges: lexical gaps, proposition types, complex syntactic structure, and
high specificity. We propose a Semantic Question Reformulation (SURF) model
offering three linguistically-grounded operations (repair, syntactic reshaping,
generalization) to rewrite questions to facilitate answering. Offline
evaluation on 1M unanswered questions from a leading voice assistant shows that
SURF significantly improves answer rates: up to 24% of previously unanswered
questions obtain relevant answers (75%). Live deployment shows positive impact
for millions of customers with unanswered questions; explicit relevance
feedback shows high user satisfaction.

该研究分析了口头问答中的常见问题，并提出了一种基于语言学方法的 “语义问题改写” 模型，该模型通过修复、句法重塑和泛化等操作，使难以回答的问题变得易于回答，大幅提高了语音助手的准确性。