| Challenge: | Especially crowdsourcing suffers from assessors having different reference frames to base their judgments on and task instructions being nondescript and therefore unhelpful in ensuring consistency. |
| Approach: | They propose an efficient annotation framework for argument quality that uses a stochastic transitivity model and an effective sampling strategy to infer high-quality labels. |
| Outcome: | The proposed model significantly outperforms existing annotation procedures and offers statistical insights into argument quality. |
Similar Papers
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
Copied to clipboard
Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| 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. |
ArgAnalysis35K : A large-scale dataset for Argument Quality Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Existing datasets in argument quality detection lack quality, quantity and diversity of topics and arguments. |
| Approach: | They propose a dataset that adds a detailed explanation of why the argument made is true, applicable or impactful. |
| Outcome: | The proposed dataset covers 34,890 high-quality argument-analysis pairs and is the largest of its kind to our knowledge. |
Bridging Argument Quality and Deliberative Quality Annotations with Adapters (2023.findings-eacl)
Copied to clipboard
| 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. |
Let’s discuss! Quality Dimensions and Annotated Datasets for Computational Argument Quality Assessment (2024.emnlp-main)
Copied to clipboard
| 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. |
Towards a Perspectivist Turn in Argument Quality Assessment (2025.naacl-long)
Copied to clipboard
| Challenge: | Argument quality is a key aspect of computational argumentation (CA), but it still exhibits a high degree of subjectivity in perception. |
| Approach: | They propose to use a multi-layered classification to target two aspects of argument quality in a systematic review of NLP datasets. |
| Outcome: | The proposed model improves the quality of annotators and their ability to be used in perspectivist research. |
A Multi-persona Framework for Argument Quality Assessment (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for argument quality assessment do not consider multi-perspective evaluation due to subjective nature of arguments. |
| Approach: | They propose a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models. |
| Outcome: | The proposed framework outperforms baselines while providing comprehensive multi-perspective rationales on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets. |
Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing (2020.coling-main)
Copied to clipboard
| 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. |
Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2023.eacl-tutorials)
Copied to clipboard
| 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 . |
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)
Copied to clipboard
| 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. |
Graph Embeddings for Argumentation Quality Assessment (2022.findings-emnlp)
Copied to clipboard
| 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. |