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utils | 1 year ago | |
README.md | 1 year ago | |
dataset_dual.py | 1 year ago | |
preprocess_data.py | 1 year ago | |
requirements.txt | 1 year ago | |
train_no_plan.py | 1 year ago | |
train_no_sns.py | 1 year ago | |
train_no_symbolic.py | 1 year ago | |
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train_with_reg.py | 1 year ago |
This repository is the implementation of Mathematical Word Problem Generation from Commonsense Knowledge
Graph and Equations
git clone https://github.com/tal-ai/MaKE_EMNLP2021.git
cd MaKE_EMNLP2021
pip install -r requirements.txt
You can train the model as follow:
python train_*.py
You can test the model as follow:
python ./test/gen_*.py
If you have any problem to the project, please feel free to report them as issues.
该算法提出一个端到端的神经网络模型,可以利用常识知识图谱和方程表达式生成丰富的数学应用题(MWP)。所提出的模型(1)从符号方程表达式和常识知识的 Levi 图中学习表征;(2)在生成MWP 时,通过自我规划模块自动融合方程式和常识知识信息。在教育场景数据集的实验表明,和其它模型相比,该方法在 MWP 生成任务上有明显优势,在自动评价指标和人类评价指标方面都优于 SOTA 模型。在教育数据集中,方程相关性分数达到 2.308,主题相关性分数达到 2.558,语言连贯性分数达到 2.505。
Text Python
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