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Existing commonsense reasoning datasets for AI and NLP tasks fail to addressan important aspect of human life: cultural differences. We introduce anapproach that extends prior work on crowdsourcing commonsense knowledge byincorporating differences in knowledge that are attributable to cultural ornational groups. We demonstrate the technique by collecting commonsenseknowledge that surrounds six fairly universal rituals -- birth, coming-of-age,marriage, funerals, new year, and birthdays -- across two national groups: theUnited States and India. Our study expands the different types of relationshipsidentified by existing work in the field of commonsense reasoning forcommonplace events, and uses these new types to gather information thatdistinguish the identity of the groups providing the knowledge. It also movesus a step closer towards building a machine that doesn't assume a rigidframework of universal (and likely Western-biased) commonsense knowledge, butrather has the ability to reason in a contextually and culturally sensitiveway. Our hope is that cultural knowledge of this sort will lead to morehuman-like performance in NLP tasks such as question answering (QA) and textunderstanding and generation.

Towards an Atlas of Cultural Commonsense for Machine Reasoning