| Challenge: | Argument Mining (AM) aims to uncover the argumentative structures within a text. |
| Approach: | They propose a method that generates argumentatively annotated text using a pretrained encoder-decoder language model and a pre-trained decoder. |
| Outcome: | The proposed method achieves state-of-the-art performance on three types of benchmark datasets. |
Similar Papers
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. |
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)
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| Challenge: | Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks. |
| Approach: | They propose a generative framework where expected outputs of AM are framed as a simple target sequence. |
| Outcome: | The proposed framework achieves state-of-the-art on two AM benchmarks. |
Argument mining as a multi-hop generative machine reading comprehension task (2023.findings-emnlp)
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| Challenge: | Argument mining is a natural language processing task that aims to generate an argumentative graph given an unstructured argumentative text. |
| Approach: | They propose a new approach which transfers the argument mining task into a multi-hop reading comprehension task by incorporating a "chain of thought" information into the model. |
| Outcome: | The proposed approach surpasses SOTA results on two arguments mining benchmarks. |
Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
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| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
| Approach: | They propose to model the three main AM sub-tasks as text generation tasks and fine-tune eight popular quantized and non-quantized large language models (LLMs) on the benchmark PE, AbstRCT, and CDCP datasets. |
| Outcome: | The proposed pipeline achieves state-of-the-art across all AM sub-tasks and datasets, showing significant improvements over previous benchmarks. |
Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining (2024.findings-acl)
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| Challenge: | Recent advances in AM models overlook the integration of supplementary discourse structure information, resulting in suboptimal outcomes. |
| Approach: | They propose a framework which generates discourse structure-aware prefixes for each layer of the generation model. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two AM benchmarks. |
Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)
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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu
| Challenge: | Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). |
| Approach: | They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts. |
| Outcome: | The proposed framework outperforms strong baselines while significantly improving explainability. |
End-to-end Argument Mining with Cross-corpora Multi-task Learning (2022.tacl-1)
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| Challenge: | Argument(ation) mining is a task of identifying argument structure from text . lack of training data makes it difficult to train models based on limited data sets. |
| Approach: | They propose an end-to-end cross-corpus argument mining method that uses auxiliary argument mining corpora to train models. |
| Outcome: | The proposed method outperforms models trained on a single corpus on arguments on arguments in argument mining tasks. |
End-to-End Argument Mining as Biaffine Dependency Parsing (2021.eacl-main)
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| Challenge: | Argumentation mining (AM) is a new field of research that uses dependency parsing to analyse arguments. |
| Approach: | They propose a neural end-to-end approach to argument mining based on dependency parsing . their model is biaffine dependency parsed and outperforms the current state-of-the-art . |
| Outcome: | The proposed model outperforms the state-of-the-art in component identification and relation identification. |
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! (C18-1)
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| Challenge: | Argumentation mining (AM) requires the identification of complex discourse structures . existing resources are not adequate for assessing cross-lingual AM due to their heterogeneity or lack of complexity. |
| Approach: | They propose to use a dataset to translate persuasive student essays into German, French, Spanish, and Chinese to compare arguments mining and annotation projection. |
| Outcome: | The proposed methods perform better when using expensive human or cheap machine translations and almost eliminate loss from cross-lingual transfer. |
Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining (2025.emnlp-main)
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| Challenge: | Argument Mining (AM) is hindered by the scarcity of structure-annotated datasets, which are expensive to create manually. |
| Approach: | They propose to use quality-oriented synthesis and diversity-oriented approach to generate argumentative texts with diverse topics and argument structures. |
| Outcome: | The proposed approach significantly improves existing models in full-data and low-resource settings. |