# Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques

arXiv: https://arxiv.org/abs/2407.03748 · HTML: https://mlsanity.com/paper/2407.03748/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017)
- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)
- [Multilingual Argument Mining: Datasets and Analysis](https://mlsanity.com/paper/2010.06432/) (2020)
- [Few-shot Learning for Named Entity Recognition in Medical Text](https://mlsanity.com/paper/1811.05468/) (2018)
- [Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!](https://mlsanity.com/paper/1807.08998/) (2018)
- [Language Independent Sequence Labelling for Opinion Target Extraction](https://mlsanity.com/paper/1901.09755/) (2019)
- [Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet](https://mlsanity.com/paper/2202.10287/) (2022)

## Built on by (most cited first, 7 of 7)

- [CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures](https://mlsanity.com/paper/2410.05235/) (2024)
- [Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models](https://mlsanity.com/paper/2503.05328/) (2025)
- [Winning with Less for Low Resource Languages: Advantage of Cross-Lingual English_Persian Argument Mining Model over LLM Augmentation](https://mlsanity.com/paper/2511.20872/) (2025)
- [Mining Legal Arguments to Study Judicial Formalism](https://mlsanity.com/paper/2512.11374/) (2025)
- [Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data](https://mlsanity.com/paper/2510.07434/) (2025)
- [MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams](https://mlsanity.com/paper/2607.19201/) (2026)
- [Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages](https://mlsanity.com/paper/2610.17894/) (2026)

## Abstract (arXiv)

Recent research on sequence labelling has been exploring different strategies to mitigate the lack of manually annotated data for the large majority of the world languages. Among others, the most successful approaches have been based on (i) the cross-lingual transfer capabilities of multilingual pre-trained language models (model-transfer), (ii) data translation and label projection (data-transfer) and (iii), prompt-based learning by reusing the mask objective to exploit the few-shot capabilities of pre-trained language models (few-shot). Previous work seems to conclude that model-transfer outperforms data-transfer methods and that few-shot techniques based on prompting are superior to updating the model's weights via fine-tuning. In this paper, we empirically demonstrate that, for Argument Mining, a sequence labelling task which requires the detection of long and complex discourse structures, previous insights on cross-lingual transfer or few-shot learning do not apply. Contrary to previous work, we show that for Argument Mining data transfer obtains better results than model-transfer and that fine-tuning outperforms few-shot methods. Regarding the former, the domain of the dataset used for data-transfer seems to be a deciding factor, while, for few-shot, the type of task (length and complexity of the sequence spans) and sampling method prove to be crucial.