# Argument Mining with Structured SVMs and RNNs

arXiv: https://arxiv.org/abs/1704.06869 · HTML: https://mlsanity.com/paper/1704.06869/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2017.

## Built on by (most cited first, 30 of 37)

- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence](https://mlsanity.com/paper/2404.11826/) (2024)
- [Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues](https://mlsanity.com/paper/2103.00820/) (2021)
- [AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach](https://mlsanity.com/paper/2305.19902/) (2023)
- [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)
- [Efficient Argument Structure Extraction with Transfer Learning and Active Learning](https://mlsanity.com/paper/2204.00707/) (2022)
- [Efficient argument classification with compact language models and ChatGPT-4 refinements](https://mlsanity.com/paper/2403.15473/) (2024)
- [Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments](https://mlsanity.com/paper/2505.22137/) (2025)
- [Randomized Deep Structured Prediction for Discourse-Level Processing](https://mlsanity.com/paper/2101.10435/) (2021)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts](https://mlsanity.com/paper/2106.14438/) (2021)
- [Argument Mining using BERT and Self-Attention based Embeddings](https://mlsanity.com/paper/2302.13906/) (2023)
- [AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining](https://mlsanity.com/paper/2309.09300/) (2023)
- [AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation](https://mlsanity.com/paper/2508.17926/) (2025)
- [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)
- [Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks](https://mlsanity.com/paper/2202.13457/) (2022)
- [LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments](https://mlsanity.com/paper/2204.01512/) (2022)
- [Sequence Graph Network for Online Debate Analysis](https://mlsanity.com/paper/2406.18696/) (2024)
- [Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information](https://mlsanity.com/paper/2310.05073/) (2023)
- [Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method](https://mlsanity.com/paper/2505.12028/) (2025)
- [A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1](https://mlsanity.com/paper/2507.08621/) (2025)
- [End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction](https://mlsanity.com/paper/2510.16363/) (2025)
- [Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection](https://mlsanity.com/paper/2603.03095/) (2026)
- [A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2](https://mlsanity.com/paper/2603.19253/) (2026)
- [Argument Mining as a Text-to-Text Generation Task](https://mlsanity.com/paper/2603.23949/) (2026)
- [From Argument Components to Graphs: A Multi-Agent Debate with Confidence Gating for Argument Relations](https://mlsanity.com/paper/2606.16047/) (2026)
- [Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition](https://mlsanity.com/paper/2209.14780/) (2022)

## Abstract (arXiv)

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.