Papers with argument mining tasks
Multilingual Argument Mining: Datasets and Analysis (2020.findings-emnlp)
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| 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. |
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. |
VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining (2023.emnlp-main)
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| Challenge: | a corpus of spoken argumentation is used to leverage audio features for argument mining tasks . a vast majority of arguments-based natural language processing resources only take text features into account . |
| Approach: | They describe a corpus of spoken argumentation created to leverage audio features for argument mining tasks. |
| Outcome: | The proposed corpus of spoken argumentation improves when integrating audio features into the argument mining pipeline. |
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)
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| Challenge: | Argument mining (AM) is a computational process that is used to analyze information in a debating system. |
| Approach: | They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks . |
| Outcome: | The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks . |
Cross-Domain Argument Quality Estimation (2023.findings-acl)
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| Challenge: | Argument mining is a field of automated discovery and organization of arguments. |
| Approach: | They propose to generalize argument quality estimation from multiple angles by combining empirical results with a training part. |
| Outcome: | The proposed method combines the results of two empirical evaluations with a training part to show that argument quality is among the more challenging tasks but can improve others. |