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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/D18-1008.pdf
Video:
 https://vimeo.com/305194870
Code
 mrlamm/textual-analogy-parsing +  additional community code