Robust Argumentation Machines : : First International Conference, RATIO 2024, Bielefeld, Germany, June 5-7, 2024, Proceedings.

• Superior document: Lecture Notes in Computer Science Series ; v.14638
• Author: Cimiano, Philipp.
• TeilnehmendeR: Frank, Anette.; Kohlhase, Michael.; Stein, Benno.
• Place / Publishing House: Cham : : Springer,, 2024.; ©2024.
• Year of Publication: 2024
• Edition: 1st ed.
• Language: English
• Series: Lecture Notes in Computer Science Series
• Physical Description: 1 online resource (379 pages)

Table of Contents

• Intro
• Preface
• Organization
• Contents
• Argument Mining
• Natural Language Hypotheses in Scientific Papers and How to Tame Them
• 1 Introduction: Scientific Hypotheses as Complex Claims
• 2 Related Work
• 2.1 Argumentation Modeling for Complex Scientific Claims
• 2.2 Knowledge Representation: Modeling Scientific Language with Knowledge Graphs
• 2.3 Hypothesis Representation in Invasion Biology
• 3 Example: The Biotic Resistance Hypothesis
• 4 Towards Formalizing Scientific Hypotheses
• 4.1 A Generic Structure for Scientific Hypotheses
• 4.2 Linking Hypothesis Formulations to Semantic Models
• 4.3 Classifying Relationships Between General and Specific Claims
• 5 Applications of the Framework
• 6 Limitations
• 7 Conclusions and Outlook
• Appendix
• References
• Weakly Supervised Claim Localization in Scientific Abstracts
• 1 Introduction
• 2 Background
• 2.1 Scientific Claim Detection
• 2.2 Input Optimization for Model Interpretability
• 3 Datasets
• 3.1 The INAS Dataset
• 3.2 The SciFact Dataset
• 4 Method
• 4.1 Span-Level Claim Evidence Localization
• 4.2 Sentence-Level Claim Evidence Localization
• 4.3 Evidence Injection
• 5 Experiments
• 5.1 Span-Level Claim Localization
• 5.2 Sentence-Level Claim Localization
• 6 Results
• 6.1 Span-Level Evidence Localization
• 6.2 Sentence-Level Evidence Localization
• 7 Conclusion
• A Experimental Details
• References
• Argument Mining of Attack and Support Patterns in Dialogical Conversations with Sequential Pattern Mining
• 1 Mining Interactions in Debates
• 2 Related Work
• 3 Predicting a Conversational Dataset
• 3.1 Corpus Creation
• 3.2 Mining Conversation Chains from Incomplete Graphs
• 3.3 Argument Abstraction by Stance and Aspect Prediction
• 3.4 Sequential Pattern Mining on Predicted Data
• 4 Results
• 4.1 Attack and Support Patterns.
• 4.2 Pattern Mining Vs. Analyzing Distributions
• 5 Conclusion
• 5.1 Limitations
• 5.2 Future Work
• References
• Cluster-Specific Rule Mining for Argumentation-Based Classification
• 1 Introduction
• 2 Background
• 3 Cluster-Specific Rule Mining
• 4 Experimental Analysis
• 5 Limitations
• 6 Conclusion
• References
• Debate Analysis and Deliberation
• Automatic Analysis of Political Debates and Manifestos: Successes and Challenges
• 1 Introduction
• 2 Fine-Grained Analysis of Political Discourse
• 2.1 Less Annotation Is More: Few-Shot Claim Classification
• 2.2 Improving Claim Classification with Hierarchical Information
• 2.3 Multilingual Claim Processing
• 2.4 Robust Actor Detection and Mapping
• 3 Coarse-Grained Analysis of Political Discourse
• 3.1 Ideological Characterization
• 3.2 Policy-Domain Characterization
• 4 Conclusions
• References
• PAKT: Perspectivized Argumentation Knowledge Graph and Tool for Deliberation Analysis 5540801En6FigaPrint.eps
• 1 Introduction
• 2 A Data Model for Perspectivized Argumentation
• 3 Constructing PAKTDDO from debate.org
• 3.1 Arguments from debate.org
• 3.2 Characterizing Arguments for Perspectivized Argumentation
• 3.3 Authors and Camps
• 3.4 Implementation and Tools for Building and Using PAKT
• 3.5 Preliminary Evaluation
• 4 Analytics Applied to PAKTDDO
• 5 Case Studies
• 5.1 Should Animal Hunting Be Banned?
• 5.2 Comparison to Other Issues
• 5.3 Argument Level
• 6 Related Work
• 7 Conclusion
• References
• PolArg: Unsupervised Polarity Prediction of Arguments in Real-Time Online Conversations
• 1 Introduction
• 2 Foundations
• 2.1 Computational Argumentation
• 2.2 Natural Language Processing
• 2.3 Online Conversation Platforms
• 3 Related Work
• 4 Prompting Strategies
• 4.1 Isolated Prompting
• 4.2 Sequential Prompting
• 4.3 Contextualized Prompting.
• 4.4 Batched Prompting
• 5 Experimental Evaluation
• 5.1 Experimental Setup
• 5.2 Datasets
• 5.3 Results and Discussion
• 5.4 Qualitative Error Analysis
• 6 Limitations
• 7 Conclusion and Future Work
• A Prompting Templates
• A.1 Isolated Prompting
• A.2 Sequential Prompting
• A.3 Contextualized Prompting
• A.4 Batched Prompting
• References
• Argument Acquisition, Annotation and Quality Assessment
• Are Large Language Models Reliable Argument Quality Annotators?
• 1 Introduction
• 2 Related Work
• 2.1 Evaluating Argument Quality
• 2.2 LLMs as Annotators
• 3 Experimental Design
• 3.1 Expert Annotation
• 3.2 Novice Annotation
• 3.3 Models
• 3.4 Prompting
• 4 Results
• 4.1 Consistency of Argument Quality Annotations
• 4.2 Agreement Between Humans and LLMs
• 4.3 LLMs as Additional Annotators
• 5 Conclusion
• 6 Limitations
• References
• The Impact of Argument Arrangement on Essay Scoring
• 1 Introduction
• 2 Related Work
• 3 Data
• 3.1 Argument-Annotated Essays Corpus
• 3.2 Feedback Corpus
• 3.3 International Corpus of Learner English
• 4 Experiments
• 4.1 ADU and Sematic Type Classification
• 4.2 Predicting Essay Quality with Flows of Semantic Types
• 4.3 Analysis of Feature Impact
• 5 Discussion
• 6 Conclusion
• References
• Finding Argument Fragments on Social Media with Corpus Queries and LLMs
• 1 Introduction
• 2 Argumentative Fragments
• 2.1 An Inventory of Logical Patterns
• 2.2 Nested Patterns
• 3 Data
• 3.1 Corpus and Linguistic Annotation
• 3.2 Manual Annotation of Argument Fragments
• 4 Corpus Queries
• 4.1 Methods
• 4.2 Evaluation and Discussion
• 5 Hierarchical Queries
• 5.1 Methods
• 5.2 Evaluation
• 5.3 Discussion
• 6 Fine-Tuning LLMs
• 6.1 Methods and Evaluation
• 6.2 Discussion: Qualitative Comparison of Approaches
• 7 Limitations
• 8 Conclusion
• References.
• Computational Models of Argumentation
• Enhancing Abstract Argumentation Solvers with Machine Learning-Guided Heuristics: A Feasibility Study
• 1 Introduction
• 2 Preliminaries
• 3 Solution Approaches in Abstract Argumentation
• 4 Machine Learning-Guided Heuristics
• 5 Experimental Analysis
• 5.1 Datasets and Setup
• 5.2 Initial Experimental Analysis
• 5.3 Evaluation and Results
• 6 Limitations
• 7 Conclusion
• References
• Ranking Transition-Based Medical Recommendations Using Assumption-Based Argumentation
• 1 Introduction
• 2 Preliminaries
• 2.1 Abstract Argumentation Frameworks
• 2.2 Ranking-Based Semantics
• 2.3 Assumption-Based Argumentation Frameworks
• 3 Ranking Assumptions
• 4 Case Study
• 5 Related Work
• 6 Limitations
• 7 Conclusion
• References
• Argumentation-Based Probabilistic Causal Reasoning
• 1 Introduction
• 2 Preliminaries
• 3 Causal Reasoning
• 3.1 Defeasible Causal Reasoning
• 3.2 Probabilistic Causal Reasoning
• 4 Counterfactual Reasoning
• 5 Discussion
• 6 Limitations
• 7 Conclusion
• References
• From Networks to Narratives: Bayes Nets and the Problems of Argumentation
• 1 Introduction
• 2 The Bayesian Approach to Argumentation
• 2.1 The Bayesian Framework
• 2.2 Bayesian Belief Networks (BBNs)
• 2.3 Explaining BBNs: Important Challenges
• 3 Algorithmic Approaches to Bayesian Argumentation
• 3.1 The Relation Between Argument Diagrams and Bayesian Networks
• 3.2 Introducing Three Extant Algorithms
• 3.3 Evaluating the Algorithms: Example Networks
• 4 Limitation
• 5 Conclusion
• References
• Enhancing Argument Generation Using Bayesian Networks
• 1 Introduction
• 2 The Question of Independent Arguments
• 2.1 Factor Graphs
• 2.2 Overview of the Factor-Graph-Approach Proposed by J. Sevilla
• 3 Testing and Improving the Factor Graph Algorithm.
• 3.1 Overview of the BARD Project and ``the Spider'' Problem
• 3.2 Results with the Original Algorithm
• 3.3 Diagnosis and Solution Proposal
• 3.4 Results of the Improved Version
• 4 Limitation and Future Work
• 5 Conclusion
• A Appendix
• References
• ``Do Not Disturb My Circles!'' Identifying the Type of Counterfactual at Hand (Short Paper)
• 1 Introduction
• 1.1 Introductory Example
• 2 Preliminaries and Related work
• 3 Backtracking in Causal Models
• 3.1 When Backtracking is not Enough
• 3.2 Iterative Backup
• 3.3 Default Logic
• 3.4 Integration of Hyperreals
• 4 Discussion
• References
• Interactive Argumentation, Recommendation and Personalization
• BEA: Building Engaging Argumentation
• 1 Introduction
• 2 Related Work
• 2.1 Argumentative Dialog Systems
• 2.2 Reflective Engagement
• 2.3 Conversational User Engagement and Virtual Avatars
• 3 Prototype and Architecture of BEA
• 3.1 System Architecture
• 3.2 User Interface
• 4 Modeling Reflective Engagement
• 5 Evaluation
• 5.1 Study 1 ch17weber2023fostering: Analyzing Focus on Challenger Arguments
• 5.2 Study 2 ch17aicherspsiva: Influence of Avatar Interface
• 6 Limitations
• 7 Conclusion and Future Work
• References
• Deciphering Personal Argument Styles - A Comprehensive Approach to Analyzing Linguistic Properties of Argument Preferences
• 1 Introduction
• 2 Background
• 2.1 Argument Data
• 2.2 Argument Preferences
• 2.3 Visual Analytics for Linguistics
• 3 The CUEPAQ Argument Exploration Pipeline
• 3.1 The CUEPipe Workflow
• 3.2 Generating a Data Set for Exploring Argument Preferences
• 3.3 Learning Preferences via Visual Interactive Labeling
• 3.4 Exploring Personal Preferences
• 4 Study: Propositional Attitudes
• 5 Limitations
• 5.1 The CUEPipe
• 5.2 The Proof-of-concept Study
• 6 Conclusion
• References
• Argument Search and Retrieval.
• Extending the Comparative Argumentative Machine: Multilingualism and Stance Detection.