Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain

04/28/2023

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by   Annerose Eichel, et al.

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We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to overcome the lack of annotated datasets. We devise a new method to aggregate salient predictions from a set of cloze query templates and show that domain-adaptation using either a small, high-quality or a customized Wikipedia corpus boosts performance. When exploring resource-lean alternatives, we find a distilled PLM clearly outperforming a classic pattern-based algorithm. Further, given that 98 domain-specific components are multiword expressions, we successfully exploit the compositionality assumption as a way to address data sparsity.