  Test data is said to be out-of-distribution (OOD) when it unexpectedly
differs from the training data, a common challenge in real-world use cases of
machine learning. Although OOD generalisation has gained interest in recent
years, few works have focused on OOD generalisation in spoken language
understanding (SLU) tasks. To facilitate research on this topic, we introduce a
modified version of the popular SLU dataset SLURP, featuring data splits for
testing OOD generalisation in the SLU task. We call our modified dataset SLURP
For OOD generalisation, or SLURPFOOD. Utilising our OOD data splits, we find
end-to-end SLU models to have limited capacity for generalisation. Furthermore,
by employing model interpretability techniques, we shed light on the factors
contributing to the generalisation difficulties of the models. To improve the
generalisation, we experiment with two techniques, which improve the results on
some, but not all the splits, emphasising the need for new techniques.
