mlsanityData to 13 Oct 2026

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

arXiv 2304.14745 ↗First posted 28 Apr 2023cs.CL cs.AI cs.IRcited by 2

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Abstract (arXiv)

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% of our domain-specific components are multiword expressions, we successfully exploit the compositionality assumption as a way to address data sparsity.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 22 of its 48 references that are in the map. First posted on arXiv 28 Apr 2023.

Builds on

22 of 48 references.

References · 22

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10 more
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Modelling Adjectival Modification Effects on Semantic Plausibilitycited by 0
  2. 2026Exploring LLMs and RAG for Plausible and Explainable Material Prediction of Vehicle Componentscited by 0