This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model~(PLM) for effectively understanding and representing mathematical problems. Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement. Typically, it requires complex mathematical logic and background knowledge for solving mathematical problems. Considering the complex nature of mathematical texts, we design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses. Specially, we first perform token-level pre-training based on a position-biased masking strategy, and then design logic-based pre-training tasks that aim to recover the shuffled sentences and formulas, respectively. Finally, we introduce a more difficult pre-training task that enforces the PLM to detect and correct the errors in its generated solutions. We conduct extensive experiments on offline evaluation (including nine math-related tasks) and online A/B test. Experimental results demonstrate the effectiveness of our approach compared with a number of competitive baselines. Our code is available at: \textcolor{blue}{\url{https://github.com/RUCAIBox/JiuZhang}}.
@article{arxiv.2206.06315,
title = {JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding},
author = {Wayne Xin Zhao and Kun Zhou and Zheng Gong and Beichen Zhang and Yuanhang Zhou and Jing Sha and Zhigang Chen and Shijin Wang and Cong Liu and Ji-Rong Wen},
journal= {arXiv preprint arXiv:2206.06315},
year = {2022}
}