“We will Reduce Taxes” Identifying Election Pledges with Language Models

Tommaso Fornaciari, Dirk Hovy, Elin Naurin, Julia Runeson, Robert Thomson, Pankaj Adhikari

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

Abstract

In an election campaign, political parties pledge to implement various projects-should they be elected. But do they follow through? To track election pledges from parties' election manifestos, we need to distinguish between pledges and general statements. In this paper, we use election manifestos of Swedish and Indian political parties to learn neural models that distinguish actual pledges from generic political positions. Since pledges might vary by election year and party, we implement a Multi-Task Learning (MTL) setup, predicting election year and manifesto's party as auxiliary tasks. Pledges can also span several sentences, so we use hierarchical models that incorporate contextual information. Lastly, we evaluate the models in a Zero-Shot Learning (ZSL) framework across countries and languages. Our results indicate that year and party have predictive power even in ZSL, while context introduces some noise. We finally discuss the linguistic features of pledges.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationACL-IJCNLP 2021
EditorsChengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
PublisherAssociation for Computational Linguistics (ACL)
Pages3406-3419
Number of pages14
ISBN (Electronic)9781954085541
Publication statusPublished - 2021
EventAnnual Meeting of the Association of Computational Linguistics and International Joint Conference on Natural Language Processing 2021 - Online, Bangkok, Thailand
Duration: 1 Aug 20216 Aug 2021
Conference number: 59th & 11th
https://aclanthology.org/2021.acl-long.0/ (Proceedings)
https://2021.aclweb.org (Website)
https://aclanthology.org/volumes/2021.findings-acl/ (Findings Proceedings)
https://aclanthology.org/2021.acl-short.100/ (Proceedings Short)

Publication series

NameFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Conference

ConferenceAnnual Meeting of the Association of Computational Linguistics and International Joint Conference on Natural Language Processing 2021
Abbreviated titleACL-IJCNLP 2021
Country/TerritoryThailand
CityBangkok
Period1/08/216/08/21
Internet address

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