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。

评估基于提示的问题回答在开放研究知识图谱中的物体预测中的应用

Evaluating Prompt-based Question Answering for Object Prediction in the  Open Research Knowledge Graph

Given a document and a target aspect (e.g., a topic of interest),
aspect-based abstractive summarization attempts to generate a summary with
respect to the aspect. Previous studies usually assume a small pre-defined set
of aspects and fall short of summarizing on other diverse topics. In this work,
we study summarizing on arbitrary aspects relevant to the document, which
significantly expands the application of the task in practice. Due to the lack
of supervision data, we develop a new weak supervision construction method and
an aspect modeling scheme, both of which integrate rich external knowledge
sources such as ConceptNet and Wikipedia. Experiments show our approach
achieves performance boosts on summarizing both real and synthetic documents
given pre-defined or arbitrary aspects.

本研究提出一种基于方面的抽象化摘要方法，可以对任意与文件相关的方面进行总结，带有外部知识库的弱监督构建方法和方面建模方案可以提高性能。