Codifying commonsense knowledge in machines is a longstanding goal of
artificial intelligence. Recently, much progress toward this goal has been made
with automatic knowledge base (KB) construction techniques. However, such
techniques focus primarily on the acquisition of positive (true) KB statements,
even though negative (false) statements are often also important for
discriminative reasoning over commonsense KBs. As a first step toward the
latter, this paper proposes NegatER, a framework that ranks potential negatives
in commonsense KBs using a contextual language model (LM). Importantly, as most
KBs do not contain negatives, NegatER relies only on the positive knowledge in
the LM and does not require ground-truth negative examples. Experiments
demonstrate that, compared to multiple contrastive data augmentation
approaches, NegatER yields negatives that are more grammatical, coherent, and
informative -- leading to statistically significant accuracy improvements in a
challenging KB completion task and confirming that the positive knowledge in
LMs can be "re-purposed" to generate negative knowledge.

该研究提出了 NegatER 框架，使用上下文语言模型对常识知识库中的负面语句进行排序，不需要真实的负面数据，有助于完成知识库中的缺失信息。实验结果证明，与多种数据扩增方法相比，NegatER 产生的负面语句更合理、更连贯、更具有信息量，以显著地提高知识库补充任务的准确性，证实了语言模型中的正面知识可以 “重新利用” 来生成负面知识。