Challenge: Existing corpora focus on specific out-of-school domains, such as legal documents.
Approach: They present a digital argumentation instruction for science corpus on 4589 essays written by 1839 german secondary school students.
Outcome: The proposed corpus is annotated according to a fine-grained annotation scheme on 4589 essays written by 1839 german secondary school students.

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A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality (2024.naacl-long)

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Challenge: Existing arguments mining corpus with ground-truth quality annotations is lacking . authors propose baseline approaches to argument mining and essay scoring .
Approach: They propose to use argumentative structure to support argumentative writing . they use an annotated german corpus to analyze interactions between the two tasks .
Outcome: The proposed methods can be used to support argumentative writing . they analyze interactions between argumentative structure and quality annotations .
CEAMC: Corpus and Empirical Study of Argument Analysis in Education via LLMs (2024.findings-emnlp)

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Challenge: Existing argument component classifications in education are simplistic and isolated, failing to capture the complete argument information.
Approach: They propose to annotate a manually annotated argument component classification dataset from authentic examination settings and to explore the performance of Large Language Models on CEAMC.
Outcome: The proposed dataset can be used to analyze argumentative essays in education.
A Corpus for Argumentative Writing Support in German (2020.coling-main)

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Challenge: In today's world most information is readily available. Consequently, the sole reproduction of information is losing attention.
Approach: They propose an annotation approach to capture claims and premises of arguments and their relations in student-written peer reviews on business models in german language.
Outcome: The proposed annotation scheme guides annotators to moderate agreement with the proposed scheme on 50 persuasive student-written peer reviews on business models.
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.
Unsupervised Argumentation Mining in Student Essays (2020.lrec-1)

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Challenge: State-of-the-art argumentation mining systems rely on annotated training data and are supervised, thus relying on an annotation of the components and relationships between them.
Approach: They propose to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues.
Outcome: The proposed approach outperforms two supervised baselines and achieves 73.5-83.7% of the performance of a state-of-the-art neural approach.
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.
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.
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.
Mining Complex Patterns of Argumentative Reasoning in Natural Language Dialogue (2025.acl-long)

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Challenge: Argumentation scheme mining is the task of automatically identifying reasoning mechanisms behind argument inferences.
Approach: They propose to create a corpus of 441 arguments annotated with 24 argumentation schemes and leverage the capabilities of LLMs and Transformer-based models to validate their applicability in real-world scenarios.
Outcome: The proposed corpus of arguments is pre-trained on a large corpus containing textbook-like argumentation schemes and validates their applicability in real-world scenarios.
Segmentation of Complex Question Turns for Argument Mining: A Corpus-based Study in the Financial Domain (2024.lrec-main)

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Challenge: Earnings Conference Calls (ECCs) are a favoured domain for the study of argumentation in context and the extraction of Argumentative Discourse Units (ADUs).
Approach: Earnings Conference Calls (ECCs) are favoured domain for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs).
Outcome: ECCs are favoured for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs).

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