Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych
| Challenge: | Argument mining is a core technology for enabling argument search in large corpora . but current methods fail when applied to heterogeneous texts . despite its obvious applications, argument search has attracted relatively little attention . |
| Approach: | They propose a system that searches sentential arguments for any given topic . ArgumenText automatically identifies and classifies arguments by relevance . |
| Outcome: | The proposed system covers 89% of arguments found in expert-curated lists . it also identifies additional valid arguments omitted or overlooked by human curators . |
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Cross-topic Argument Mining from Heterogeneous Sources (D18-1)
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| Challenge: | Argument mining is a core technology for automating argument search in document collections. |
| Approach: | They propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. |
| Outcome: | The proposed scheme outperforms vanilla BiLSTMs in two- and three-label cross-topic settings and can be further improved by leveraging additional data for topic relevance using multi-task learning. |
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. |
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. |
| Outcome: | The proposed systems are more robust to natural language variations than existing arguments mining systems. |
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. |
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. |
| Outcome: | The proposed method dispenses with heavy feature and model engineering and allows for new state-of-the-art performance for its three main sub-tasks. |
Argument Mining for Understanding Peer Reviews (N19-1)
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| Challenge: | In 2015 alone, approximately 63.4 million hours were spent on peer reviews. |
| Approach: | They propose to automatically detect argumentative propositions put forward by reviewers and their types by automatically detecting their types and types. |
| Outcome: | The proposed method detects (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). |
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
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| Challenge: | Existing methods for analyzing discourse-level argument annotations require expensive labor and data. |
| Approach: | They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators. |
| Outcome: | The proposed method can be applied even by untrained annotators. |
Towards an argumentative content search engine using weak supervision (C18-1)
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| Challenge: | Existing work focused on detecting claims within a small set of documents . however, pinpointing relevant claims within massive unstructured corpora, received little attention. |
| Approach: | They propose to use a weak signal to develop a query for claim–sentence detection using a large text corpus. |
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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. |
| 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. |
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 . |
| Outcome: | The proposed models perform best on 17 English sentence-level datasets on common tasks, but their performance drops when applied to unseen datasets. |