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.

我们提出了一种新的方法，通过探究预训练的语言模型（PLMs）中的填充式任务设置来学习车辆维修领域中组件的特定于领域的可能材料，以克服缺少注释数据集的问题。我们设计了一种新的方法来汇总一组填充式查询模板中显著的预测结果，并表明使用小型、高质量或定制的维基百科语料库的领域自适应可以提高性能。进一步地，鉴于我们领域特定的组件中 98％都是多个单词表达式，我们成功地利用组成假设作为解决数据稀疏性的方法。