  An essential requirement for a real-world Knowledge Base Question Answering
(KBQA) system is the ability to detect answerability of questions when
generating logical forms. However, state-of-the-art KBQA models assume all
questions to be answerable. Recent research has found that such models, when
superficially adapted to detect answerability, struggle to satisfactorily
identify the different categories of unanswerable questions, and simultaneously
preserve good performance for answerable questions. Towards addressing this
issue, we propose RetinaQA, a new KBQA model that unifies two key ideas in a
single KBQA architecture: (a) discrimination over candidate logical forms,
rather than generating these, for handling schema-related unanswerability, and
(b) sketch-filling-based construction of candidate logical forms for handling
data-related unaswerability. Our results show that RetinaQA significantly
outperforms adaptations of state-of-the-art KBQA models in handling both
answerable and unanswerable questions and demonstrates robustness across all
categories of unanswerability. Notably, RetinaQA also sets a new
state-of-the-art for answerable KBQA, surpassing existing models.
