  We introduce CORE, a dataset for few-shot relation classification (RC)
focused on company relations and business entities. CORE includes 4,708
instances of 12 relation types with corresponding textual evidence extracted
from company Wikipedia pages. Company names and business entities pose a
challenge for few-shot RC models due to the rich and diverse information
associated with them. For example, a company name may represent the legal
entity, products, people, or business divisions depending on the context.
Therefore, deriving the relation type between entities is highly dependent on
textual context. To evaluate the performance of state-of-the-art RC models on
the CORE dataset, we conduct experiments in the few-shot domain adaptation
setting. Our results reveal substantial performance gaps, confirming that
models trained on different domains struggle to adapt to CORE. Interestingly,
we find that models trained on CORE showcase improved out-of-domain
performance, which highlights the importance of high-quality data for robust
domain adaptation. Specifically, the information richness embedded in business
entities allows models to focus on contextual nuances, reducing their reliance
on superficial clues such as relation-specific verbs. In addition to the
dataset, we provide relevant code snippets to facilitate reproducibility and
encourage further research in the field.

Computation and Language