Learn to Solve Algebra Word Problems Using Quadratic Programming
Learn to Solve Algebra Word Problems Using Quadratic Programming
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DOI：
10.18653/v1/d15-1096
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发表时间：
2015-09
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通讯作者：
Lipu Zhou;Shuaixiang Dai;Liwei Chen
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文献类型：
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作者：
Lipu Zhou;Shuaixiang Dai;Liwei Chen
本文提出了一种自动求解代数题的新算法。我们的算法通过分析一个包含所有可能的方程系统的假设空间来解决一个单词问题，该假设空间是通过将单词问题中的数字分配到从训练数据中提取的一组方程系统模板中来生成的。为了获得一个鲁棒决策面，我们训练了一个对数线性模型，使正确分配和错误分配之间的余量尽可能大。这就形成了一个可有效求解的二次规划（QP）问题。实验结果表明，我们的算法达到了79.7%的准确率，比最先进的基线高出约10% （Kushman et al., 2014）。
This paper presents a new algorithm to automatically solve algebra word problems. Our algorithm solves a word problem via analyzing a hypothesis space containing all possible equation systems generated by assigning the numbers in the word problem into a set of equation system templates extracted from the training data. To obtain a robust decision surface, we train a log-linear model to make the margin between the correct assignments and the false ones as large as possible. This results in a quadratic programming (QP) problem which can be efficiently solved. Experimental results show that our algorithm achieves 79.7% accuracy, about 10% higher than the state-of-the-art baseline (Kushman et al., 2014).