'We will Reduce Taxes' - Identifying Election Pledges with Language Models


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.

In Findings of ACL: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)