Answering Unanswered Questions through Semantic Reformulations in Spoken QA

• URL: http://arxiv.org/abs/2305.17393v2
• Date: Sat, 3 Jun 2023 05:40:25 GMT
• Title: Answering Unanswered Questions through Semantic Reformulations in Spoken QA
• Authors: Pedro Faustini, Zhiyu Chen, Besnik Fetahu, Oleg Rokhlenko and Shervin Malmasi
• Abstract summary: Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. 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.
• Score: 20.216161323866867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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