Challenge: a tutorial on computational argumentation is updated to address the problem of argument quality . argument quality is a field of interdisciplinary research that connects natural language processing to social sciences .
Approach: They present an updated version of the EACL 2023 tutorial on argument quality . they will focus on the notions of argument quality across disciplines .
Outcome: The updated version of the EACL 2023 tutorial focuses on argument quality assessment . the authors will focus on the interface between Argument Mining and Deliberation Theory .

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Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2023.eacl-tutorials)

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Challenge: a tutorial on argument quality assessment will focus on what makes an argument good or bad . argument quality is a field encompassing varying tasks on the automated analysis and synthesis of natural language arguments.
Approach: This tutorial will focus on the assessment of argument quality across disciplines . authors will involve participants in annotation studies on the quality assessment .
Outcome: The tutorial will focus on the assessment of argument quality across disciplines . it will involve participants in two annotation studies on the quality assessment and the improvement of quality .
Towards Argument Mining for Social Good: A Survey (2021.acl-long)

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Challenge: Argument Mining is a social science-based approach to analysis and analysis of arguments.
Approach: They propose a novel definition of argument quality which integrates the social science literature and the argument quality.
Outcome: The proposed definition of argument quality integrates the social science literature and the argument quality debate.
Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing (2020.coling-main)

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Challenge: Existing work on argument quality (AQ) focuses on overall quality, but there is no large-scale theory-based corpus and corresponding computational models.
Approach: They propose to use a large-scale English multi-domain argumentative writing corpus annotated with theory-based AQ scores to assess argument quality.
Outcome: The proposed methods improve argument quality in three domains and can be used as strong baselines for future work.
Argument Quality Assessment in the Age of Instruction-Following Large Language Models (2024.lrec-main)

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Challenge: Argument quality assessment is critical for opinion formation, decision making, writing education, and the like.
Approach: They propose to use large language models to leverage knowledge across contexts to enable a much more reliable assessment.
Outcome: The proposed approach improves the quality of argumentation and the ability to leverage knowledge across contexts.
Let’s discuss! Quality Dimensions and Annotated Datasets for Computational Argument Quality Assessment (2024.emnlp-main)

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Challenge: Argumentation is a key competence and an important cultural technique in democratic societies.
Approach: They propose to create domain-specific datasets and methods to assess argument quality.
Outcome: The proposed methods address gaps in the literature and aid future research in the domain.
Graph Embeddings for Argumentation Quality Assessment (2022.findings-emnlp)

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Challenge: Argumentation is the process by which arguments are constructed, compared, evaluated in several respects and judged in order to establish whether any of them is warranted.
Approach: They propose to annotate 1908 arguments tagged with quality facets from a resource of 402 persuasive essays and to use them to create a neural architecture that takes into account the support and attack relations holding among the arguments.
Outcome: The proposed neural architecture outperforms state-of-the-art and standard arguments on the persuasive essays dataset.
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)

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Challenge: 6.3k arguments were collected from contributors of various levels, and are released as part of this work.
Approach: They propose to use a language model to annotate arguments for argument ranking and argument-pair classification.
Outcome: The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task.
Bridging Argument Quality and Deliberative Quality Annotations with Adapters (2023.findings-eacl)

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Challenge: Assessing the quality of an argument is a complex, highly subjective task . argument quality dimensions are complex and dependent on the context in which it is assessed .
Approach: They propose a multi-task learning framework that incorporates knowledge about related dimensions into the learning process.
Outcome: The proposed framework improves quality prediction in an extrinsic, out-of-domain task.
The Shift from Logic to Dialectic in Argumentation Theory: Implications for Computational Argument Quality Assessment (2025.coling-main)

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Challenge: In the field of computational argument quality assessment, logic and dialectic are essential dimensions used to measure the quality of argumentative texts.
Approach: They propose to separate logic and dialectic as quality dimensions in computational argument quality assessment . they propose to use dialectical considerations to improve the quality of argumentative texts .
Outcome: The proposed method can benefit argument theory and argument analysis by separating the two quality dimensions.
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.

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