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In this work, we study whether multilingual language models (MultiLMs) cantransfer logical reasoning abilities to other languages when they arefine-tuned for reasoning in a different language. We evaluate the cross-lingualreasoning abilities of MultiLMs in two schemes: (1) where the language of thecontext and the question remain the same in the new languages that are tested(i.e., the reasoning is still monolingual, but the model must transfer thelearned reasoning ability across languages), and (2) where the language of thecontext and the question is different (which we term code-switched reasoning).On two logical reasoning datasets, RuleTaker and LeapOfThought, we demonstratethat although MultiLMs can transfer reasoning ability across languages in amonolingual setting, they struggle to transfer reasoning abilities in acode-switched setting. Following this observation, we propose a novel attentionmechanism that uses a dedicated set of parameters to encourage cross-lingualattention in code-switched sequences, which improves the reasoning performanceby up to 14% and 4% on the RuleTaker and LeapOfThought datasets, respectively.

Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention