To understand a sentence like "whereas only 10% of White Americans live at or
below the poverty line, 28% of African Americans do" it is important not only
to identify individual facts, e.g., poverty rates of distinct demographic
groups, but also the higher-order relations between them, e.g., the disparity
between them. In this paper, we propose the task of Textual Analogy Parsing
(TAP) to model this higher-order meaning. The output of TAP is a frame-style
meaning representation which explicitly specifies what is shared (e.g., poverty
rates) and what is compared (e.g., White Americans vs. African Americans, 10%
vs. 28%) between its component facts. Such a meaning representation can enable
new applications that rely on discourse understanding such as automated chart
generation from quantitative text. We present a new dataset for TAP, baselines,
and a model that successfully uses an ILP to enforce the structural constraints
of the problem.

本文介绍了文本类比分析的任务，提出了一种以框架为风格的含义表示方法，它可以明确指定其组成要素之间共享的内容和比较的对象，从而支持从数量型文本生成自动化图表和其他依赖于话语理解的新应用。同时介绍了一个新的 TAP 数据集，包括基线和成功使用 ILP 实施结构约束的模型。