Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts
Matthew Lamm, Arun Chaganty, Christopher D. Manning, Dan Jurafsky, Percy Liang
Abstract
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. Given a sentence such as the one above, TAP outputs 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.- Anthology ID:
- D18-1008
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 82–92
- Language:
- URL:
- https://aclanthology.org/D18-1008
- DOI:
- 10.18653/v1/D18-1008
- Cite (ACL):
- Matthew Lamm, Arun Chaganty, Christopher D. Manning, Dan Jurafsky, and Percy Liang. 2018. Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 82–92, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts (Lamm et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/D18-1008.pdf
- Code
- mrlamm/textual-analogy-parsing + additional community code