@inproceedings{tsai-etal-2021-mining,
    title = "Mining Commonsense and Domain Knowledge from Math Word Problems",
    author = "Tsai, Shih-Hung  and
      Liang, Chao-Chun  and
      Wang, Hsin-Min  and
      Su, Keh-Yih",
    editor = "Lee, Lung-Hao  and
      Chang, Chia-Hui  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
    month = oct,
    year = "2021",
    address = "Taoyuan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.rocling-1.15/",
    pages = "111--117",
    abstract = "Current neural math solvers learn to incorporate commonsense or domain knowledge by utilizing pre-specified constants or formulas. However, as these constants and formulas are mainly human-specified, the generalizability of the solvers is limited. In this paper, we propose to explicitly retrieve the required knowledge from math problemdatasets. In this way, we can determinedly characterize the required knowledge andimprove the explainability of solvers. Our two algorithms take the problem text andthe solution equations as input. Then, they try to deduce the required commonsense and domain knowledge by integrating information from both parts. We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving."
}