  In this work, we study whether multilingual language models (MultiLMs) can
transfer logical reasoning abilities to other languages when they are
fine-tuned for reasoning in a different language. We evaluate the cross-lingual
reasoning abilities of MultiLMs in two schemes: (1) where the language of the
context 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 the
learned reasoning ability across languages), and (2) where the language of the
context and the question is different (which we term code-switched reasoning).
On two logical reasoning datasets, RuleTaker and LeapOfThought, we demonstrate
that although MultiLMs can transfer reasoning ability across languages in a
monolingual setting, they struggle to transfer reasoning abilities in a
code-switched setting. Following this observation, we propose a novel attention
mechanism that uses a dedicated set of parameters to encourage cross-lingual
attention in code-switched sequences, which improves the reasoning performance
by up to 14% and 4% on the RuleTaker and LeapOfThought datasets, respectively.
