Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis (N18-1)
Copied to clipboard
| Challenge: | Election manifestos document the intentions, motives, and views of political parties. |
| Approach: | They propose a hierarchical structured deep model to predict fine- and coarse-grained positions and a probabilistic soft logic model to perform post-hoc calibration of coarse- and fine-grain positions. |
| Outcome: | The proposed model outperforms state-of-the-art approaches at both granularities using manifestos from twelve countries, written in ten different languages. |
Similar Papers
Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on political positioning on Twitter has tended to focus on manifestos rather than social media since it is ambiguous and dependent on social context. |
| Approach: | They propose to use hashtags as a signal to fine-tune text representations for politicians' tweets using a hashtag-based method to predict pairwise positional similarities between parties from the manifesto case to the Twitter case. |
| Outcome: | The proposed method matches politicians' statements to official lines of the parties' tweets, even when only small subsets from shorter time periods are available. |
Deep Ordinal Regression for Pledge Specificity Prediction (D19-1)
Copied to clipboard
| Challenge: | Currently, there are no publicly available annotated datasets of pledges . a novel approach to specificity prediction is needed to predict the specificity of pledged issues. |
| Approach: | They propose deep ordinal regression approaches for specificity prediction using supervised and semi-supervised settings. |
| Outcome: | The proposed methods demonstrate their utility over several baseline approaches. |
Identifying Fine-grained Forms of Populism in Political Discourse: A Case Study on Donald Trump’s Presidential Campaigns (2026.eacl-long)
Copied to clipboard
| Challenge: | Large Language Models excel in a wide range of instruction-following tasks, but their grasp of social science concepts remains underexplored. |
| Approach: | They evaluate pre-trained large language models to identify populist discourse . they use a RoBERTa classifier to analyze campaign speeches by Donald Trump . |
| Outcome: | The proposed model outperforms all new-era instruction-tuned LLMs on populist discourse analysis. |
Additive manifesto decomposition: A policy domain aware method for understanding party positioning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for estimating policy domain aware party similarity are limited to global domains. |
| Approach: | They propose a workflow for estimating policy domain aware party similarity by aggregating policy domains into a single figure . they use a set of tools to extract party positions on major policy axes via multidimensional scaling. |
| Outcome: | The proposed method yields high correlation when predicting party similarity at a global level and provides accurate party-specific positions even with automatically labelled policy domains. |
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining (D19-1)
Copied to clipboard
| Challenge: | Existing work on fine grained opinion annotations rely only on coarsely labeled opinions. |
| Approach: | They propose to use hierarchical structure of opinions to build a fine and coarse grained opinion model that exploits different views of the opinion expression. |
| Outcome: | The proposed model outperforms existing models on a recently released multimodal fine grained annotated corpus on IMDB and social networks. |
Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers (2023.emnlp-main)
Copied to clipboard
| Challenge: | Scaling analysis is a technique that assigns a political actor a score on a predefined scale based on 'typically long' text. |
| Approach: | They propose to use label aggregation and long-input-Transformer-based models to automatically scale political-party manifestos. |
| Outcome: | The proposed models can scale political platforms on a predefined scale based on 'left-right' scales and work robustly across domains and languages. |
LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings. |
| Approach: | They propose to analyze 156k LLM responses to 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. |
| Outcome: | The proposed analysis of 156k LLM responses to the Political Compass Test (PCT) generated by 6 LLMs shows that tropes are recurrent and consistent across prompts. |
“We will Reduce Taxes” - Identifying Election Pledges with Language Models (2021.findings-acl)
Copied to clipboard
| Challenge: | a political party's manifestos are published before any election, but do they follow through? a new study uses neural models to distinguish between actual pledges and general statements . |
| Approach: | They use election manifestos of Swedish and Indian political parties to learn neural models that distinguish actual pledges from generic positions. |
| Outcome: | The proposed model can predict election year and manifesto's party, while context introduces noise. |
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)
Copied to clipboard
| Challenge: | a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types. |
| Approach: | They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates . |
| Outcome: | The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates . |
Improving LLM Generations via Fine-Grained Self-Endorsement (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent large language models (LLMs) have demonstrated remarkable capabilities but can still fail frequently on knowledge-intensive tasks. |
| Approach: | They propose a self-endorsement framework that leverages fine-grained fact-level comparisons across multiple sampled responses. |
| Outcome: | The proposed framework can improve factuality of generations with simple prompts across scales of LLMs. |