@inproceedings{wu-nakayama-2022-weakly,
    title = "Weakly Supervised Formula Learner for Solving Mathematical Problems",
    author = "Wu, Yuxuan  and
      Nakayama, Hideki",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.coling-1.150/",
    pages = "1743--1752",
    abstract = "Mathematical reasoning task is a subset of the natural language question answering task. Existing work suggested solving this task with a two-phase approach, where the model first predicts formulas from questions and then calculates answers from such formulas. This approach achieved desirable performance in existing work. However, its reliance on annotated formulas as intermediate labels throughout its training limited its application. In this work, we put forward the idea to enable models to learn optimal formulas autonomously. We proposed Weakly Supervised Formula Learner, a learning framework that drives the formula exploration with weak supervision from the final answers to mathematical problems. Our experiments are conducted on two representative mathematical reasoning datasets MathQA and Math23K. On MathQA, our method outperformed baselines trained on complete yet imperfect formula annotations. On Math23K, our method outperformed other weakly supervised learning methods."
}