# Multilingual Argument Mining: Datasets and Analysis

arXiv: https://arxiv.org/abs/2010.06432 · HTML: https://mlsanity.com/paper/2010.06432/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 20 of 20)

- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [Multilingual Counter Narrative Type Classification](https://mlsanity.com/paper/2109.13664/) (2021)
- [Project Debater APIs: Decomposing the AI Grand Challenge](https://mlsanity.com/paper/2110.01029/) (2021)
- [Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques](https://mlsanity.com/paper/2407.03748/) (2024)
- [NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes](https://mlsanity.com/paper/2402.14458/) (2024)
- [Towards a Perspectivist Turn in Argument Quality Assessment](https://mlsanity.com/paper/2502.14501/) (2025)
- [ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation](https://mlsanity.com/paper/2505.22076/) (2025)
- [Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts](https://mlsanity.com/paper/2106.14438/) (2021)
- [Topic Ontologies for Arguments](https://mlsanity.com/paper/2301.09759/) (2023)
- [Political claim identification and categorization in a multilingual setting: First experiments](https://mlsanity.com/paper/2310.09256/) (2023)
- [Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation](https://mlsanity.com/paper/2402.03642/) (2024)
- [Multi-Domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2205.03804/) (2022)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)
- [Mining Legal Arguments to Study Judicial Formalism](https://mlsanity.com/paper/2512.11374/) (2025)
- [Distant finetuning with discourse relations for stance classification](https://mlsanity.com/paper/2204.12693/) (2022)
- [Overview of the CAIL 2023 Argument Mining Track](https://mlsanity.com/paper/2406.14503/) (2024)
- [CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures](https://mlsanity.com/paper/2606.06646/) (2026)
- [Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages](https://mlsanity.com/paper/2610.17894/) (2026)

## Abstract (arXiv)

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.