Toward an Atlas of Cultural Commonsense for Machine Reasoning

Mark Finlayson

Abstract:

Existing commonsense reasoning datasets for AI and NLP
tasks fail to address an important aspect of human life: cul-
tural differences. We introduce an approach that extends prior
work on crowdsourcing commonsense knowledge by incor-
porating differences in knowledge that are attributable to cul-
tural or national groups. We demonstrate the technique by
collecting commonsense knowledge that surrounds six fairly
universal rituals—birth, coming-of-age, marriage, funerals,
new year, and birthdays—across two national groups: the
United States and India. Our study expands the different types
of relationships identified by existing work in the field of
commonsense reasoning for commonplace events, and uses
these new types to gather information that distinguish the
identity of the groups providing the knowledge. It also moves
us a step closer towards building a machine that doesn’t as-
sume a rigid framework of universal (and likely Western-
biased) commonsense knowledge, but rather has the ability
to reason in a contextually and culturally sensitive way. Our
hope is that cultural knowledge of this sort will lead to more
human-like performance in NLP tasks such as question an-
swering (QA) and text understanding and generation.