# AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

arXiv: https://arxiv.org/abs/2004.14677 · HTML: https://mlsanity.com/paper/2004.14677/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 30 of 39)

- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining](https://mlsanity.com/paper/2106.00829/) (2021)
- [IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks](https://mlsanity.com/paper/2203.12257/) (2022)
- [Argument Undermining: Counter-Argument Generation by Attacking Weak Premises](https://mlsanity.com/paper/2105.11752/) (2021)
- [Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics](https://mlsanity.com/paper/2506.12657/) (2025)
- [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)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Must Read: A Comprehensive Survey of Computational Persuasion](https://mlsanity.com/paper/2505.07775/) (2025)
- [ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining](https://mlsanity.com/paper/2209.06416/) (2022)
- [Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues](https://mlsanity.com/paper/2103.00820/) (2021)
- [ENTRUST: Argument Reframing with Language Models and Entailment](https://mlsanity.com/paper/2103.06758/) (2021)
- [ArgU: A Controllable Factual Argument Generator](https://mlsanity.com/paper/2305.05334/) (2023)
- [Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing](https://mlsanity.com/paper/2302.05039/) (2023)
- [A Hybrid Intelligence Method for Argument Mining](https://mlsanity.com/paper/2403.09713/) (2024)
- ["Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space](https://mlsanity.com/paper/2101.10952/) (2021)
- [Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation](https://mlsanity.com/paper/2112.06196/) (2021)
- [Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?](https://mlsanity.com/paper/2203.12881/) (2022)
- [Efficient Argument Structure Extraction with Transfer Learning and Active Learning](https://mlsanity.com/paper/2204.00707/) (2022)
- [Contextualizing Argument Quality Assessment with Relevant Knowledge](https://mlsanity.com/paper/2305.12280/) (2023)
- [Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining](https://mlsanity.com/paper/2310.12172/) (2023)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [Contextual Argument Component Classification for Class Discussions](https://mlsanity.com/paper/2102.10290/) (2021)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [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)
- [Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome](https://mlsanity.com/paper/2404.13792/) (2024)
- [Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies](https://mlsanity.com/paper/2503.16544/) (2025)
- [ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers](https://mlsanity.com/paper/2202.06690/) (2022)
- [Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models](https://mlsanity.com/paper/2405.12884/) (2024)

## Abstract (arXiv)

Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one's argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.