Catchphrase: Automatic Detection of Cultural References

Nir Sweed, Dafna Shahaf


Abstract
A snowclone is a customizable phrasal template that can be realized in multiple, instantly recognized variants. For example, “* is the new *" (Orange is the new black, 40 is the new 30). Snowclones are extensively used in social media. In this paper, we study snowclones originating from pop-culture quotes; our goal is to automatically detect cultural references in text. We introduce a new, publicly available data set of pop-culture quotes and their corresponding snowclone usages and train models on them. We publish code for Catchphrase, an internet browser plugin to automatically detect and mark references in real-time, and examine its performance via a user study. Aside from assisting people to better comprehend cultural references, we hope that detecting snowclones can complement work on paraphrasing and help tackling long-standing questions in social science about the dynamics of information propagation.
Anthology ID:
2021.acl-short.1
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://aclanthology.org/2021.acl-short.1
DOI:
10.18653/v1/2021.acl-short.1
Bibkey:
Cite (ACL):
Nir Sweed and Dafna Shahaf. 2021. Catchphrase: Automatic Detection of Cultural References. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 1–7, Online. Association for Computational Linguistics.
Cite (Informal):
Catchphrase: Automatic Detection of Cultural References (Sweed & Shahaf, ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.1.pdf
Video:
 https://aclanthology.org/2021.acl-short.1.mp4