@inproceedings{tan-etal-2022-investigating,
    title = "Investigating Math Word Problems using Pretrained Multilingual Language Models",
    author = "Tan, Minghuan  and
      Wang, Lei  and
      Jiang, Lingxiao  and
      Jiang, Jing",
    editor = "Ferreira, Deborah  and
      Valentino, Marco  and
      Freitas, Andre  and
      Welleck, Sean  and
      Schubotz, Moritz",
    booktitle = "Proceedings of the 1st Workshop on Mathematical Natural Language Processing (MathNLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.mathnlp-1.2",
    doi = "10.18653/v1/2022.mathnlp-1.2",
    pages = "7--16",
    abstract = "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 the 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 share the same numerical constants and operator set. However, it can be better generalized if problem types exist on both source language and target language.",
}