RETINAQA : A Knowledge Base Question Answering Model Robust to both
  Answerable and Unanswerable Questions
Prayushi Faldu Indrajit Bhattacharya Mausam
State-of-the-art KBQA models assume answerability of questions. Recent
research has shown that while these can be adapted to detect unaswerability
with suitable training and thresholding, this comes at the expense of accuracy
for answerable questions, and no single model is able to handle all categories
of unanswerability. We propose a new model for KBQA named RetinaQA that is
robust against unaswerability. It complements KB-traversal based logical form
retrieval with sketch-filling based logical form construction. This helps with
questions that have valid logical forms but no data paths in the KB leading to
an answer. Additionally, it uses discrimination instead of generation to better
identify questions that do not have valid logical forms. We demonstrate that
RetinaQA significantly outperforms adaptations of state-of-the-art KBQA models
across answerable and unanswerable questions, while showing robustness across
unanswerability categories. Remarkably, it also establishes a new state-of-the
art for answerable KBQA by surpassing existing models