Investigating Math Word Problems using Pretrained Multilingual Language Models
This work addresses language transfer challenges in math problem-solving for multilingual AI applications, but it is incremental as it builds on existing models and datasets.
The paper tackled math word problems by evaluating cross-lingual and multilingual solvers based on pretrained language models, finding that transfer fails when languages differ even with similar operators, but improves when problem types overlap across languages.
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.