Handling Ontology Gaps in Semantic Parsing
<|reference_start|>Handling Ontology Gaps in Semantic Parsing: 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.<|reference_end|>
@article{bacciu2024handling,
    title={Handling Ontology Gaps in Semantic Parsing},
    author={Andrea Bacciu, Marco Damonte, Marco Basaldella, Emilio Monti},
    journal={arXiv preprint arXiv:2406.19537},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19537},
    primaryClass={cs.CL cs.AI}
}