# Political claim identification and categorization in a multilingual setting: First experiments

arXiv: https://arxiv.org/abs/2310.09256 · HTML: https://mlsanity.com/paper/2310.09256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Corpus Wide Argument Mining -- a Working Solution](https://mlsanity.com/paper/1911.10763/) (2019)
- [What is the Essence of a Claim? Cross-Domain Claim Identification](https://mlsanity.com/paper/1704.07203/) (2017)
- [Multilingual Argument Mining: Datasets and Analysis](https://mlsanity.com/paper/2010.06432/) (2020)
- [Detecting Media Bias in News Articles using Gaussian Bias Distributions](https://mlsanity.com/paper/2010.10649/) (2020)
- [Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!](https://mlsanity.com/paper/1807.08998/) (2018)
- [Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study](https://mlsanity.com/paper/1906.10519/) (2019)

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

- [Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability](https://mlsanity.com/paper/2412.11653/) (2024)
- [Enriching Social Science Research via Survey Item Linking](https://mlsanity.com/paper/2412.15831/) (2024)

## Abstract (arXiv)

The identification and classification of political claims is an important step in the analysis of political newspaper reports; however, resources for this task are few and far between. This paper explores different strategies for the cross-lingual projection of political claims analysis. We conduct experiments on a German dataset, DebateNet2.0, covering the policy debate sparked by the 2015 refugee crisis. Our evaluation involves two tasks (claim identification and categorization), three languages (German, English, and French) and two methods (machine translation -- the best method in our experiments -- and multilingual embeddings).