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.

本文探讨了跨语言和多语言下的数学应用问题，使用预训练的多语言模型构建模型，并使用序列到序列模型进行求解。通过比较跨语言和多语言场景下数学应用问题的求解情况，表明将模型迁移到不同的语言上可能会导致性能下降，但如果问题类型存在于源语言和目标语言中，则其性能可以得到完善提升。

使用预训练多语言语言模型研究数学问题