| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
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External Knowledge-Driven Argument Mining: Leveraging Attention-Enhanced Multi-Network Models (2024.emnlp-main)
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| Challenge: | Argument mining involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs). |
| Approach: | They propose to leverage external resources to identify semantic paths linking ADUs . they propose to use WordNet, ConceptNet, and Wikipedia to identify these paths . |
| Outcome: | The proposed architecture achieves F-scores of 0.85, 0.84, 0.70, and 0.87 on four datasets. |
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining? (2022.acl-long)
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| Challenge: | Existing methods for argument mining are limited by the scarcity of manually annotated data and the highly domain-dependent nature of argumentation. |
| Approach: | They propose a novel transfer learning strategy to fine tune pretrained Transformer-based Language Models on a selectively masked language modeling task and a new prompt-based strategy for inter-component relation prediction. |
| Outcome: | The proposed method outperforms existing models on both within- and out-of-domain datasets while leveraging on the discourse context. |
Exploring the Role of Argument Structure in Online Debate Persuasion (2020.emnlp-main)
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| Challenge: | Existing work in NLP has shown that linguistic features extracted from debate text and features encoding the characteristics of the audience are both critical in persuasion studies. |
| Approach: | They propose to incorporate argument structure features into an LSTM-based model to assess the persuasiveness of debates. |
| Outcome: | The proposed model incorporates argument structure features to predict debaters that make the most convincing arguments on online debate forums. |
Argumentation Mining on Essays at Multi Scales (2020.coling-main)
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| Challenge: | Argumentation mining on essays is a new task in natural language processing. |
| Approach: | They propose a multi-scale argumentation mining model which aims to identify the types and locations of argumentation components from essay text. |
| Outcome: | The proposed model outperforms existing models on mining all types of argumentation components on the Persuasive Essay dataset. |
On the Role of Key Phrases in Argument Mining (2025.findings-naacl)
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| Challenge: | Existing approaches to argument mining often overlook crucial conceptual links between ACs and ARs. |
| Approach: | They propose a framework that extracts key phrases from AM benchmarks using an open-source Large Language Model. |
| Outcome: | The proposed framework surpasses baselines on three structurally distinct AM benchmarks by up to 9.5% F1 score, demonstrating its strong potential. |
Contextual Argument Component Classification for Class Discussions (2020.coling-main)
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| Challenge: | Argument mining systems often consider contextual information when training to perform tasks such as argument component identification, classification, and relation extraction. |
| Approach: | They propose to incorporate speaker context and local discourse context into a model for classifying argument components in multi-party classroom discussions. |
| Outcome: | The proposed model improves when varying context size and position . the results support the claim that context size is important . |
Unveiling the Power of Argument Arrangement in Online Persuasive Discussions (2023.findings-emnlp)
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| Challenge: | a recent study shows that the CMV is the best time period in human history for the vast majority of people. |
| Approach: | They extend a semantic argumentation unit type model by clustering type sequences into different argument arrangement patterns and representing discussions as sequences of these patterns. |
| Outcome: | The proposed model outperforms existing classifiers on the change my view forum discussion data. |
“Tell me who you are and I tell you how you argue”: Predicting Stances and Arguments for Stakeholder Groups (2024.findings-naacl)
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| Challenge: | Argument mining has focused on the identification, extraction, and formalization of arguments. |
| Approach: | They propose a framework that relies on a recommender-based architecture to predict stances and argumentative main points on societally controversial topics for a given stakeholder. |
| Outcome: | The proposed framework predicts arguments on a debate topic based on BERTScore and debate.org datasets. |
A Neural Transition-based Model for Argumentation Mining (2021.acl-long)
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| Challenge: | Existing methods for identifying argumentation structures are inefficient and class imbalanced. |
| Approach: | They propose a neural transition-based model that incrementally builds an argumentation graph by generating a sequence of actions. |
| Outcome: | The proposed model can handle tree and non-tree structured argumentation without structural constraints. |
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |