English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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