@inproceedings{eichel-etal-2023-made,
    title = "Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain",
    author = "Eichel, Annerose  and
      Schlipf, Helena  and
      Schulte im Walde, Sabine",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2023.eacl-main.104/",
    doi = "10.18653/v1/2023.eacl-main.104",
    pages = "1420--1435",
    abstract = "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."
}