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        <title>Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts</title>
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        <namePart type="given">Matthew</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1008</identifier>
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%0 Conference Proceedings
%T Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts
%A Lamm, Matthew
%A Chaganty, Arun
%A Manning, Christopher D.
%A Jurafsky, Dan
%A Liang, Percy
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lamm-etal-2018-textual
%X 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.
%R 10.18653/v1/D18-1008
%U https://aclanthology.org/D18-1008
%U https://doi.org/10.18653/v1/D18-1008
%P 82-92

Markdown (Informal)

[Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts](https://aclanthology.org/D18-1008) (Lamm et al., EMNLP 2018)

• Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts (Lamm et al., EMNLP 2018)

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.