Commonsense knowledge is crucial for artificial intelligence systems to
understand natural language. Previous commonsense knowledge acquisition
approaches typically rely on human annotations (for example, ATOMIC) or text
generation models (for example, COMET.) Human annotation could provide
high-quality commonsense knowledge, yet its high cost often results in
relatively small scale and low coverage. On the other hand, generation models
have the potential to automatically generate more knowledge. Nonetheless,
machine learning models often fit the training data well and thus struggle to
generate high-quality novel knowledge. To address the limitations of previous
approaches, in this paper, we propose an alternative commonsense knowledge
acquisition framework DISCOS (from DIScourse to COmmonSense), which
automatically populates expensive complex commonsense knowledge to more
affordable linguistic knowledge resources. Experiments demonstrate that we can
successfully convert discourse knowledge about eventualities from ASER, a
large-scale discourse knowledge graph, into if-then commonsense knowledge
defined in ATOMIC without any additional annotation effort. Further study
suggests that DISCOS significantly outperforms previous supervised approaches
in terms of novelty and diversity with comparable quality. In total, we can
acquire 3.4M ATOMIC-like inferential commonsense knowledge by populating ATOMIC
on the core part of ASER. Codes and data are available at
this https URL

本文提出了一种基于 DISCOS 算法的常识知识获取框架，能够将更昂贵的复杂常识知识自动转化为更可承担的语言知识资源，以此来获取大规模常识推理知识。此方法在 novelty 和 diversity 方面表现优越，拥有着与监督学习方法相当的质量。