  Commonsense reasoning (CSR) requires the model to be equipped with general
world knowledge. While CSR is a language-agnostic process, most comprehensive
knowledge sources are in few popular languages, especially English. Thus, it
remains unclear how to effectively conduct multilingual commonsense reasoning
(XCSR) for various languages. In this work, we propose to utilize English
knowledge sources via a translate-retrieve-translate (TRT) strategy. For
multilingual commonsense questions and choices, we collect related knowledge
via translation and retrieval from the knowledge sources. The retrieved
knowledge is then translated into the target language and integrated into a
pre-trained multilingual language model via visible knowledge attention. Then
we utilize a diverse of 4 English knowledge sources to provide more
comprehensive coverage of knowledge in different formats. Extensive results on
the XCSR benchmark demonstrate that TRT with external knowledge can
significantly improve multilingual commonsense reasoning in both zero-shot and
translate-train settings, outperforming 3.3 and 3.6 points over the previous
state-of-the-art on XCSR benchmark datasets (X-CSQA and X-CODAH).
