Challenge: Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data.
Approach: They propose a set of guidelines for the annotation of argument schemes and a new annotation tool for the 'inferential' argument schemes.
Outcome: The proposed corpus includes 112 argumentative microtexts and a new annotation tool.

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Annotating Arguments in a Corpus of Opinion Articles (2022.lrec-1)

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Challenge: Argument annotation is the process of exposing and justifying one's points of view, with the aim of conveying a logical reasoning through a set of semantically related propositions.
Approach: They propose to use argumentative discourse units to annotate arguments in Portuguese using a multi-layered process to analyze the annotations produced.
Outcome: The proposed model exploits the best practices identified in previous studies while fostering the potential use of the resulting annotated corpus for new purposes.
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.
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.
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Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method (2025.acl-long)

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Challenge: Existing research on argument mining has proposed various argument annotation schemes and tasks.
Approach: They propose a framework comprising 14 fine-grained relation types to capture the interplay between argument components for a thorough understanding of argument structure.
Outcome: The proposed framework captures the interplay between argument components for a thorough understanding of argument structure.
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)

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Challenge: a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types.
Approach: They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates .
Outcome: The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates .
Implicit Knowledge in Argumentative Texts: An Annotated Corpus (2020.lrec-1)

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Challenge: Especially in argumentative texts, people omit information that seems clear and evident . a computational system typically does not possess commonsense or domain-specific knowledge to reconstruct implied information.
Approach: They build a corpus of human annotations of missing and implied information in argumentative texts.
Outcome: The proposed dataset can help to assist automated argument analysis and guide the process of revealing implicit information in argumentative texts automatically.
Corpus for Modeling User Interactions in Online Persuasive Discussions (2020.lrec-1)

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Challenge: Several studies have focused on the identification and classification of argumentative components and the argumentative relations between the components.
Approach: They propose an annotation scheme and corpus that captures user-generated inner-post arguments and inter-post relations between users in ChangeMyView.
Outcome: The proposed annotation scheme captures user-generated inner-post arguments and inter-post relations in ChangeMyView, a persuasive forum.
The Discussion Tracker Corpus of Collaborative Argumentation (2020.lrec-1)

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Challenge: The Discussion Tracker corpus is an annotated dataset of transcripts of spoken, multi-party argumentation transcribed from 985 minutes of audio .
Approach: They analyze 29 multi-party arguments transcribed from 985 minutes of audio . they provide descriptive statistics and code for predicting each dimension separately.
Outcome: The Discussion Tracker corpus was collected in high school English classes and annotated for argument moves, specificity, specificities and collaboration dimensions.
Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays (P18-1)

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Challenge: Existing work on automated essay scoring has focused on holistic scoring, which summarizes the quality of an essay with a single score.
Approach: They present a corpus of essays simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness.
Outcome: The proposed corpus could trigger the development of novel computational models that provide useful feedback to students on why their arguments are (un)persuasive .
An Environment for Relational Annotation of Political Debates (P19-3)

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Challenge: Scalable text analysis techniques can open corpora to new questions in computational social sciences and digital humanities.
Approach: They describe a tool that allows annotating newspaper text with rich information about claims (demands) raised by politicians and other actors.
Outcome: The MARDY tool realizes the complete workflow necessary for annotating a large newspaper text collection with rich information about claims (demands) raised by politicians and other actors.

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