# Neural End-to-End Learning for Computational Argumentation Mining

arXiv: https://arxiv.org/abs/1704.06104 · HTML: https://mlsanity.com/paper/1704.06104/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 30 of 41)

- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks](https://mlsanity.com/paper/2203.12257/) (2022)
- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (2021)
- [Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes](https://mlsanity.com/paper/2105.07571/) (2021)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate](https://mlsanity.com/paper/2408.04472/) (2024)
- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach](https://mlsanity.com/paper/2305.19902/) (2023)
- [Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques](https://mlsanity.com/paper/2407.03748/) (2024)
- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)
- [A Hybrid Intelligence Method for Argument Mining](https://mlsanity.com/paper/2403.09713/) (2024)
- [Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checks](https://mlsanity.com/paper/2503.00024/) (2025)
- [Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?](https://mlsanity.com/paper/2203.12881/) (2022)
- [Identifying Informational Sources in News Articles](https://mlsanity.com/paper/2305.14904/) (2023)
- [HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine](https://mlsanity.com/paper/2306.06029/) (2023)
- [Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets](https://mlsanity.com/paper/2205.11472/) (2022)
- [Contextual Argument Component Classification for Class Discussions](https://mlsanity.com/paper/2102.10290/) (2021)
- [AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining](https://mlsanity.com/paper/2309.09300/) (2023)
- [Multi-Agent Dialectical Refinement for Enhanced Argument Classification](https://mlsanity.com/paper/2603.27451/) (2026)
- ["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)
- [Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining](https://mlsanity.com/paper/2312.00874/) (2023)
- [A Cascade Model for Argument Mining in Japanese Political Discussions: the QA Lab-PoliInfo-3 Case Study](https://mlsanity.com/paper/2207.01672/) (2022)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)
- [End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction](https://mlsanity.com/paper/2510.16363/) (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)
- [Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection](https://mlsanity.com/paper/2603.03095/) (2026)
- [Argument Mining as a Text-to-Text Generation Task](https://mlsanity.com/paper/2603.23949/) (2026)
- [Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text](https://mlsanity.com/paper/2112.00160/) (2021)

## Abstract (arXiv)

We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance.