| Challenge: | Argument mining tasks in non-English languages are dominated by English . we use a pre-trained language model that supports 104 languages to train models . |
| Approach: | They propose a multilingual BERT model to address argument mining tasks in non-English languages . they use English datasets and machine translation to facilitate transfer learning . |
| Outcome: | The proposed model is well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments. |
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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. |
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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. |
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Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments (2025.acl-long)
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| Challenge: | Identifying arguments is a prerequisite for various tasks in automated discourse analysis. |
| Approach: | They evaluate four BERT-like transformers on 17 English sentence-level datasets . they find that they tend to rely on lexical shortcuts tied to content words . |
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Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain (2024.lrec-main)
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| Challenge: | Argument mining is a complex process that requires a large amount of resources and time. |
| Approach: | They propose to analyze arguments in three different languages and domains to understand their robustness to natural language variations. |
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Transferring Confluent Knowledge to Argument Mining (2022.coling-1)
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| Challenge: | Argument mining is a natural language processing task that seeks to obtain structured arguments from unstructured text. |
| Approach: | They propose to use a transfer learning methodology to assess the potential of argument mining knowledge with confluent tasks. |
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Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)
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| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
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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. |
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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
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CEAMC: Corpus and Empirical Study of Argument Analysis in Education via LLMs (2024.findings-emnlp)
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Yupei Ren, Hongyi Wu, Zhaoguang Long, Shangqing Zhao, Xinyi Zhou, Zheqin Yin, Xinlin Zhuang, Xiaopeng Bai, Man Lan
| Challenge: | Existing argument component classifications in education are simplistic and isolated, failing to capture the complete argument information. |
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Can Large Language Models perform Relation-based Argument Mining? (2025.coling-main)
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| Challenge: | Existing methods for RbAM fail to perform satisfactorily across different datasets. |
| Approach: | They propose to use relation-based argument mining to determine agreement (support) and disagreement (attack) relations amongst textual arguments in binary and ternary settings. |
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