  In this paper, we revisit math word problems~(MWPs) from the cross-lingual
and multilingual perspective. We construct our MWP solvers over pretrained
multilingual language models using sequence-to-sequence model with copy
mechanism. We compare how the MWP solvers perform in cross-lingual and
multilingual scenarios. To facilitate the comparison of cross-lingual
performance, we first adapt the large-scale English dataset MathQA as a
counterpart of the Chinese dataset Math23K. Then we extend several English
datasets to bilingual datasets through machine translation plus human
annotation. Our experiments show that the MWP solvers may not be transferred to
a different language even if the target expressions have the same operator set
and constants. But for both cross-lingual and multilingual cases, it can be
better generalized if problem types exist on both source language and target
language.
