An arithmetic word problem typically includes a textual description
containing several constant quantities. The key to solving the problem is to
reveal the underlying mathematical relations (such as addition and subtraction)
among quantities, and then generate equations to find solutions. This work
presents a novel approach, Quantity Tagger, that automatically discovers such
hidden relations by tagging each quantity with a sign corresponding to one type
of mathematical operation. For each quantity, we assume there exists a latent,
variable-sized quantity span surrounding the quantity token in the text, which
conveys information useful for determining its sign. Empirical results show
that our method achieves 5 and 8 points of accuracy gains on two datasets
respectively, compared to prior approaches.

该论文介绍了一种新方法 Quantity Tagger，用于解决算术问题，通过对数量打标签以自动发现隐藏的数学关系，进而生成方程以寻找解，并在两个数据集上相比之前的方法，分别取得了 5 和 8 点的准确率改进。

数量标注器：一种隐变量序列标记方法用于解决加减法文字问题

Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving  Addition-Subtraction Word Problems

Identifying mathematical relations expressed in text is essential to
understanding a broad range of natural language text from election reports, to
financial news, to sport commentaries to mathematical word problems. This paper
focuses on identifying and understanding mathematical relations described
within a single sentence. We introduce the problem of Equation Parsing -- given
a sentence, identify noun phrases which represent variables, and generate the
mathematical equation expressing the relation described in the sentence. We
introduce the notion of projective equation parsing and provide an efficient
algorithm to parse text to projective equations. Our system makes use of a high
precision lexicon of mathematical expressions and a pipeline of structured
predictors, and generates correct equations in $70\%$ of the cases. In $60\%$
of the time, it also identifies the correct noun phrase $\rightarrow$ variables
mapping, significantly outperforming baselines. We also release a new annotated
dataset for task evaluation.

本文讨论了在单个句子中识别和理解数学关系表示的问题，提出了方程式解析的问题，并介绍了一种高效的算法来解析文本到射影方程。我们的系统利用数学表达式的高精度词典和一系列的结构化预测器，在 70% 的情况下可以生成正确的方程，在 60% 的时间里，还可以识别正确的名词短语和变量映射，显著优于基线。我们还发布了一个新的注释数据集以供任务评估。