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Towards a Formal Framework for Evaluating Explanations in Automated Fact Verification


Core Concepts
This paper proposes a formal framework for conceptualizing and evaluating different types of rationalizing explanations (free-form, deductive, and argumentative) in the context of automated fact verification.
Abstract
The paper presents a formal framework for conceptualizing and evaluating different types of rationalizing explanations in the context of automated fact verification. The key highlights are: The paper defines three classes of rationalizing explanations: free-form, deductive, and argumentative. These explanations vary in their level of structure, from free-text to logically connected propositions to argumentative frameworks. For each explanation class, the paper defines desirable properties that can be used to evaluate the quality of the explanations. These include coherence, non-circularity, relevance, and non-redundancy. The paper provides concrete metrics to quantify the satisfaction of these properties, enabling systematic evaluation of explanations. The framework is grounded in the automated fact verification task, but the authors argue it can be applied more broadly to evaluate explanations for various NLP models. The key contribution is the systematic formalization of explanation types and properties, which can guide the development of more transparent and trustworthy NLP systems.
Stats
The paper does not contain any key metrics or figures. It focuses on defining a conceptual framework and properties for evaluating explanations.
Quotes
There are no direct quotes from the content that are particularly striking or support the key arguments.

Deeper Inquiries

What are some potential challenges in applying this framework to real-world NLP systems, beyond the automated fact verification task

One potential challenge in applying this framework to real-world NLP systems beyond automated fact verification is the scalability and adaptability of the framework. Real-world NLP systems often deal with a wide range of tasks and domains, each requiring specific types of explanations. Adapting the framework to accommodate these diverse requirements while maintaining consistency and effectiveness could be a significant challenge. Additionally, the complexity of NLP models and the dynamic nature of language could pose challenges in ensuring that the framework remains relevant and applicable over time.

How could this framework be extended to account for human preferences and cultural differences in the understanding and evaluation of explanations

To account for human preferences and cultural differences in the understanding and evaluation of explanations, the framework could be extended by incorporating a cultural sensitivity component. This could involve analyzing how different cultural backgrounds and preferences influence the perception and interpretation of explanations. By integrating cultural factors into the evaluation metrics, the framework could provide more nuanced assessments that consider the diverse perspectives of users. Additionally, incorporating user feedback mechanisms and conducting user studies across different cultural groups could help tailor the framework to better align with human preferences.

Are there other desirable properties for rationalizing explanations that could be incorporated into this framework

Some other desirable properties for rationalizing explanations that could be incorporated into this framework include transparency, interpretability, and user-centricity. Transparency refers to the clarity and openness of the explanation, ensuring that the reasoning behind the model's prediction is easily understandable. Interpretability focuses on the ease of understanding and interpreting the explanation, making it accessible to users with varying levels of expertise. User-centricity emphasizes the alignment of the explanation with user needs and preferences, ensuring that the explanation is tailored to the user's context and requirements. By incorporating these properties, the framework can enhance the quality and usability of rationalizing explanations in NLP systems.
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