# Multi-Task Learning for Argumentation Mining in Low-Resource Settings

arXiv: https://arxiv.org/abs/1804.04083 · HTML: https://mlsanity.com/paper/1804.04083/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers](https://mlsanity.com/paper/2102.06008/) (2021)
- [Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?](https://mlsanity.com/paper/2203.12881/) (2022)
- [AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation](https://mlsanity.com/paper/2508.17926/) (2025)
- ["Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays](https://mlsanity.com/paper/2103.04518/) (2021)
- [Multi-Task and Multi-Corpora Training Strategies to Enhance Argumentative Sentence Linking Performance](https://mlsanity.com/paper/2109.13067/) (2021)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)
- [Multi-Task Learning Improves Performance In Deep Argument Mining Models](https://mlsanity.com/paper/2307.01401/) (2023)
- [Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification](https://mlsanity.com/paper/2209.02495/) (2022)
- [Learning from Auxiliary Sources in Argumentative Revision Classification](https://mlsanity.com/paper/2309.07334/) (2023)

## Abstract (arXiv)

We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.