[Submitted on 10 Jul 2024]
View a PDF of the paper titled Out-of-distribution generalisation in spoken language understanding, by Dejan Porjazovski and 2 other authors
Abstract: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.
Submission history
From: Dejan Porjazovski [view email]
[v1]
Wed, 10 Jul 2024 07:27:38 UTC (1,127 KB)
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