Structured knowledge is important for many AI applications. Commonsense
knowledge, which is crucial for robust human-centric AI, is covered by a small
number of structured knowledge projects. However, they lack knowledge about
human traits and behaviors conditioned on socio-cultural contexts, which is
crucial for situative AI. This paper presents CANDLE, an end-to-end methodology
for extracting high-quality cultural commonsense knowledge (CCSK) at scale.
CANDLE extracts CCSK assertions from a huge web corpus and organizes them into
coherent clusters, for 3 domains of subjects (geography, religion, occupation)
and several cultural facets (food, drinks, clothing, traditions, rituals,
behaviors). CANDLE includes judicious techniques for classification-based
filtering and scoring of interestingness. Experimental evaluations show the
superiority of the CANDLE CCSK collection over prior works, and an extrinsic
use case demonstrates the benefits of CCSK for the GPT-3 language model. Code
and data can be accessed at this https URL.

本文提出了一个用于在大规模上提取高质量文化常识知识 (CCsk) 的端到端方法，名为 CANDLE，该方法从 Web 语料库中提取 CCsk 断言并将它们组织成相符的类群，包括地理、宗教、职业等 3 个领域，多个文化方面。通过分类过滤和趣味分数评估方法，CANDLE 优于先前的作品，并且实验评估演示了 CCsk 对 GPT-3 语言模型带来的好处。