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.