XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning

Understand

In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects.

• Moreover, they should be able to generalise the acquired world knowledge to new languages, modulo cultural differences.
• Advances in machine reasoning and cross-lingual transfer depend on the availability of challenging evaluation benchmarks.
• Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, which includes resource-poor languages like Eastern Apur\'imac Quechua and Haitian Creole.