# Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers

arXiv: https://arxiv.org/abs/2310.12575 · HTML: https://mlsanity.com/paper/2310.12575/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Text-Based Ideal Points](https://mlsanity.com/paper/2005.04232/) (2020)
- [On Cross-Lingual Retrieval with Multilingual Text Encoders](https://mlsanity.com/paper/2112.11031/) (2021)
- [TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages](https://mlsanity.com/paper/2211.16496/) (2022)
- [Political Text Scaling Meets Computational Semantics](https://mlsanity.com/paper/1904.06217/) (2019)
- [Optimizing text representations to capture (dis)similarity between political parties](https://mlsanity.com/paper/2210.11989/) (2022)
- [Additive manifesto decomposition: A policy domain aware method for understanding party positioning](https://mlsanity.com/paper/2305.10136/) (2023)

## Built on by (most cited first, 2 of 2)

- [What Is The Political Content in LLMs' Pre- and Post-Training Data?](https://mlsanity.com/paper/2509.22367/) (2025)
- [Revisiting the Exit from Nuclear Energy in Germany with NLP](https://mlsanity.com/paper/2408.13810/) (2024)

## Abstract (arXiv)

Scaling analysis is a technique in computational political science that assigns a political actor (e.g. politician or party) a score on a predefined scale based on a (typically long) body of text (e.g. a parliamentary speech or an election manifesto). For example, political scientists have often used the left--right scale to systematically analyse political landscapes of different countries. NLP methods for automatic scaling analysis can find broad application provided they (i) are able to deal with long texts and (ii) work robustly across domains and languages. In this work, we implement and compare two approaches to automatic scaling analysis of political-party manifestos: label aggregation, a pipeline strategy relying on annotations of individual statements from the manifestos, and long-input-Transformer-based models, which compute scaling values directly from raw text. We carry out the analysis of the Comparative Manifestos Project dataset across 41 countries and 27 languages and find that the task can be efficiently solved by state-of-the-art models, with label aggregation producing the best results.