Learning Scalar Adjective Intensity from Paraphrases

Anne Cocos, Skyler Wharton, Ellie Pavlick, Marianna Apidianaki, Chris Callison-Burch

Abstract
Adjectives like “warm”, “hot”, and “scalding” all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair “really hot” <–> “scalding” suggests that “hot” < “scalding”. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to “yes/no” questions.
Anthology ID:
D18-1202
Original:
D18-1202v1
Version 2:
D18-1202v2
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:
1752–1762
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/D18-1202/
DOI:
10.18653/v1/D18-1202
Bibkey:
Cite (ACL):
Anne Cocos, Skyler Wharton, Ellie Pavlick, Marianna Apidianaki, and Chris Callison-Burch. 2018. Learning Scalar Adjective Intensity from Paraphrases. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1752–1762, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Learning Scalar Adjective Intensity from Paraphrases (Cocos et al., EMNLP 2018)
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