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.