  The majority of Neural Semantic Parsing (NSP) models are developed with the
assumption that there are no concepts outside the ones such models can
represent with their target symbols (closed-world assumption). This assumption
leads to generate hallucinated outputs rather than admitting their lack of
knowledge. Hallucinations can lead to wrong or potentially offensive responses
to users. Hence, a mechanism to prevent this behavior is crucial to build
trusted NSP-based Question Answering agents. To that end, we propose the
Hallucination Simulation Framework (HSF), a general setting for stimulating and
analyzing NSP model hallucinations. The framework can be applied to any NSP
task with a closed-ontology. Using the proposed framework and KQA Pro as the
benchmark dataset, we assess state-of-the-art techniques for hallucination
detection. We then present a novel hallucination detection strategy that
exploits the computational graph of the NSP model to detect the NSP
hallucinations in the presence of ontology gaps, out-of-domain utterances, and
to recognize NSP errors, improving the F1-Score respectively by ~21, ~24% and
~1%. This is the first work in closed-ontology NSP that addresses the problem
of recognizing ontology gaps. We release our code and checkpoints at
https://github.com/amazon-science/handling-ontology-gaps-in-semantic-parsing.
