Papers with Argument Mining
Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2024.lrec-tutorials)
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| Challenge: | a tutorial on computational argumentation is updated to address the problem of argument quality . argument quality is a field of interdisciplinary research that connects natural language processing to social sciences . |
| Approach: | They present an updated version of the EACL 2023 tutorial on argument quality . they will focus on the notions of argument quality across disciplines . |
| Outcome: | The updated version of the EACL 2023 tutorial focuses on argument quality assessment . the authors will focus on the interface between Argument Mining and Deliberation Theory . |
The Open Argument Mining Framework (2025.acl-demo)
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Debela Gemechu, Ramon Ruiz-Dolz, Kamila Górska, Somaye Moslemnejad, Eimear Maguire, Dimitra Zografistou, Yohan Jo, John Lawrence, Chris Reed
| Challenge: | Argument Mining (AM) has been a key area of research for many years, but it is still a challenging field. |
| Approach: | the oAMF provides an open-source, modular platform that unifies diverse AM methods. |
| Outcome: | the oAMF is an open-source, modular, and scalable platform that unifies diverse AM methods. |
Towards Argument Mining for Social Good: A Survey (2021.acl-long)
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| Challenge: | Argument Mining is a social science-based approach to analysis and analysis of arguments. |
| Approach: | They propose a novel definition of argument quality which integrates the social science literature and the argument quality. |
| Outcome: | The proposed definition of argument quality integrates the social science literature and the argument quality debate. |
Argument Mining as a Text-to-Text Generation Task (2024.eacl-long)
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| 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. |
Bridging Argument Quality and Deliberative Quality Annotations with Adapters (2023.findings-eacl)
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| Challenge: | Assessing the quality of an argument is a complex, highly subjective task . argument quality dimensions are complex and dependent on the context in which it is assessed . |
| Approach: | They propose a multi-task learning framework that incorporates knowledge about related dimensions into the learning process. |
| Outcome: | The proposed framework improves quality prediction in an extrinsic, out-of-domain task. |
Argument Component Segmentation with Fine-Tuned Large Language Models (2026.findings-eacl)
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| Challenge: | Argument Mining (AM) aims to identify and interpret argumentative structures in unstructured text. |
| Approach: | They propose a fine-grained, paired-tag annotation schema that distinguishes between relevant and surrounding content. |
| Outcome: | The proposed approach performs comparable to human expert annotators across multiple benchmark datasets. |
DREAM: Deployment of Recombination and Ensembles in Argument Mining (2023.emnlp-main)
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| Challenge: | Current approaches to Argument Mining (AM) take a holistic view of the overall pipeline. |
| Approach: | They propose a framework that allows for the (automated) combination of AM components instead of all-new solutions. |
| Outcome: | The proposed framework outperforms the best single systems in terms of accuracy measured by an AM benchmark. |
Reports of personal experiences and stories in argumentation: datasets and analysis (2022.acl-long)
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| Challenge: | Personal experiences and stories are important in argumentation, but they are not considered in the social sciences. |
| Approach: | They propose to use annotated documents to scale-up the analysis using existing annotations. |
| Outcome: | The proposed classifiers can identify documents containing personal experiences and reports . they can scale up to three domains and show that they perform well across domains. |
Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates (P19-1)
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| Challenge: | Political debates are a natural application scenario for Argument Mining. |
| Approach: | They propose an argument mining approach to political debates that uses argument components to annotate 39 political debate from the last 50 years of US presidential campaigns. |
| Outcome: | The proposed approach outperforms baselines in argument mining over political debates. |
Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques (2024.acl-long)
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| Challenge: | Recent work on sequence labelling has explored different strategies to mitigate the lack of manually annotated data for the large majority of the world languages. |
| Approach: | They propose to use the mask objective to exploit the few-shot capabilities of pre-trained language models to improve their performance. |
| Outcome: | The proposed model-transfer outperforms data-transference and fine-tuning outperformed few-shot methods for Argument Mining task. |
How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering (2025.acl-long)
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Irina Nikishina, Saba Anwar, Nikolay Dolgov, Maria Manina, Daria Ignatenko, Artem Shelmanov, Chris Biemann
| Challenge: | Comparative Question Answering (CQA) is a task that involves processing information and diverse viewpoints. |
| Approach: | They construct a dataset of arguments annotated with their relevance and use it to answer comparative questions. |
| Outcome: | The proposed dataset contains arguments annotated with their relevance and enables precise traceability and faithfulness. |
CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining (2025.acl-long)
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| Challenge: | Argument Mining (AM) involves the automatic identification of argument structure in natural language. |
| Approach: | They propose an approach that captures local and global coherence to identify argument structures by modeling macro-structure. |
| Outcome: | The proposed approach shows superior performance on heterogeneous datasets and on unseen datasets. |
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. |
Argument Summarization and its Evaluation in the Era of Large Language Models (2025.emnlp-main)
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Moritz Altemeyer, Steffen Eger, Johannes Daxenberger, Yanran Chen, Tim Altendorf, Philipp Cimiano, Benjamin Schiller
| Challenge: | Large Language Models (LLMs) have revolutionized various Natural Language Generation tasks, including Argument Summarization (ArgSum). |
| Approach: | They propose a prompt-based evaluation scheme and validate it through a human benchmark dataset. |
| Outcome: | The proposed evaluation scheme outperforms existing methods and is validated by a human benchmark dataset. |