There have been many recent investigations into prompt-based training of
transformer language models for new text genres in low-resource settings. The
prompt-based training approach has been found to be effective in generalizing
pre-trained or fine-tuned models for transfer to resource-scarce settings. This
work, for the first time, reports results on adopting prompt-based training of
transformers for \textit{scholarly knowledge graph object prediction}. The work
is unique in the following two main aspects. 1) It deviates from the other
works proposing entity and relation extraction pipelines for predicting objects
of a scholarly knowledge graph. 2) While other works have tested the method on
text genera relatively close to the general knowledge domain, we test the
method for a significantly different domain, i.e. scholarly knowledge, in turn
testing the linguistic, probabilistic, and factual generalizability of these
large-scale transformer models. We find that (i) per expectations, transformer
models when tested out-of-the-box underperform on a new domain of data, (ii)
prompt-based training of the models achieve performance boosts of up to 40\% in
a relaxed evaluation setting, and (iii) testing the models on a starkly
different domain even with a clever training objective in a low resource
setting makes evident the domain knowledge capture gap offering an
empirically-verified incentive for investing more attention and resources to
the scholarly domain in the context of transformer models.

本文报告了采用基于提示的 transformer 训练方法进行学术知识图谱对象预测的结果，发现在轻度评估设置下，这种训练方法能够提高 40％的性能，但仍存在领域知识捕获差距，需要更多关注和资源来解决这个问题。