Argumentation is a fundamental aspect of human communication, and various frameworks have been developed to analyze and construct effective arguments: Aristotelian, Rogerian, Toulmin, Narrative, and Fallacy-based. Furthermore, these frameworks can be enhanced using Graph Neural Networks (GNNs), particularly within the context of textual entailment.
The Aristotelian framework, rooted in classical rhetoric, emphasizes persuasion through a combination of logical reasoning (logos), ethical appeal (ethos), and emotional appeal (pathos). It follows a structured approach, moving from an introduction and statement of the case to providing proof, refuting opposing arguments, and concluding with a strong peroration. This framework is well-suited for persuasive speeches and debates where a clear stance is essential.
In contrast, the Rogerian argument prioritizes finding common ground and reducing conflict. Developed by Carl Rogers, this approach involves understanding the opponent's perspective, acknowledging its validity, and working towards a mutually acceptable solution. Rogerian arguments are effective in situations where parties hold strongly opposing views and compromise is necessary.
The Toulmin model, proposed by Stephen Toulmin, focuses on the practical structure of everyday arguments. It breaks down an argument into six key components: claim, grounds, warrant, backing, qualifier, and rebuttal. This model provides a flexible framework for analyzing and constructing arguments in various contexts, highlighting the importance of evidence, justification, and acknowledging limitations.
Narrative arguments utilize storytelling to persuade, employing elements like plot, characters, setting, and theme. This approach can be particularly powerful in engaging the audience's emotions and conveying complex ideas through relatable narratives. Narrative arguments find applications in fields like law, where stories can shape perceptions of a case, and in marketing, where they forge emotional connections with consumers.
Finally, fallacy-based argumentation centers on identifying and avoiding logical fallacies - flaws in reasoning that weaken or invalidate arguments. By understanding common fallacies such as ad hominem, straw man, and slippery slope, individuals can construct stronger arguments and effectively critique the arguments of others. This framework is crucial for critical thinking and ensuring the validity of claims.
Applying GNNs to Textual Entailment
Textual entailment, the task of determining whether one text (premise) logically entails another (hypothesis), can be enhanced by integrating these argumentation frameworks with Graph Neural Networks (GNNs) and knowledge graphs. GNNs are neural network architectures designed to operate on graph-structured data, making them well-suited for representing the relationships between words, sentences, and concepts within arguments.
Here's how GNNs can be applied:
- Knowledge Graph Construction: A knowledge graph can be constructed to represent relevant background knowledge, concepts, and relationships related to the premise and hypothesis. Entities in the texts can be linked to nodes in the knowledge graph, and relationships between entities can be represented as edges.
- Argument Graph Representation: The premise and hypothesis can be parsed and represented as a graph, where nodes represent words or phrases, and edges represent syntactic or semantic relationships. Argumentation frameworks can inform the design of this graph. For instance, in a Toulmin-based graph, nodes could represent claims, grounds, and warrants, while edges could represent the inferential connections between them.
- GNN-based Reasoning: A GNN can be trained on the constructed graph to learn node representations that capture the semantic and argumentative relationships between the premise and hypothesis. The GNN can propagate information across the graph, allowing it to reason about the entailment relation.
- Entailment Prediction: The learned node representations can be used to predict whether the premise entails the hypothesis. This can be achieved by feeding the representations into a classifier that outputs an entailment probability.
For example, consider the sentence "A woman is playing the piano" entails "A person is playing a musical instrument". A GNN can be constructed where nodes represent "woman", "playing", "piano", "person", "musical instrument" and edges capture relationships like "is-a" (woman is-a person) and "part-of" (piano part-of musical instrument) from a knowledge graph. The GNN can then reason over this graph to infer the entailment relation.
Various argumentation frameworks offer valuable tools for constructing and analyzing arguments, each with its own strengths and applications. GNNs, combined with knowledge graphs, provide a powerful means of implementing these frameworks in computational tasks like textual entailment, enabling more sophisticated and nuanced reasoning over textual data.
