# Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts

arXiv: https://arxiv.org/abs/1809.02700 · HTML: https://mlsanity.com/paper/1809.02700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024)
- [AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies](https://mlsanity.com/paper/2402.12370/) (2024)
- [When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP](https://mlsanity.com/paper/2305.14007/) (2023)
- [Part & Whole Extraction: Towards A Deep Understanding of Quantitative Facts for Percentages in Text](https://mlsanity.com/paper/2110.13505/) (2021)
- [FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data](https://mlsanity.com/paper/2502.18471/) (2025)

## Abstract (arXiv)

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.