Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts
This addresses the need for discourse understanding in applications like automated chart generation from quantitative text, but it is incremental as it builds on existing parsing tasks.
The paper tackles the problem of understanding higher-order relations in analogical statements by proposing Textual Analogy Parsing (TAP) to explicitly model shared and compared elements, resulting in a new dataset, baselines, and a model using ILP for structural constraints.
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.