  Commonsense reasoning research has so far been limited to English. We aim to
evaluate and improve popular multilingual language models (ML-LMs) to help
advance commonsense reasoning (CSR) beyond English. We collect the Mickey
Corpus, consisting of 561k sentences in 11 different languages, which can be
used for analyzing and improving ML-LMs. We propose Mickey Probe, a
language-agnostic probing task for fairly evaluating the common sense of
popular ML-LMs across different languages. In addition, we also create two new
datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other
languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense
reasoning. To improve the performance beyond English, we propose a simple yet
effective method -- multilingual contrastive pre-training (MCP). It
significantly enhances sentence representations, yielding a large performance
gain on both benchmarks.
