7 papers
Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification
Géraud Faye, Benjamin Icard, Morgane Casanova +6
Among news disorders, propagandist news are particularly insidious, because they tend to mix oriented messages with factual reports intended to look like reliable news. To detect p…
Fairness Interventions in Classification: A Study on AI Explainability
Thomas Souverain, Johnathan Nguyen, Nicolas Meric +1
This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, an…
Explaining vague language
Paul Ãgré, Benjamin Spector
Why is language vague? Vagueness may be explained and rationalized if it can be shown that vague language is more useful to speaker and hearer than precise language. In a well-know…
Probabilistic consequence relations
Paul Ãgré, Ellie Ripley
This paper investigates logical consequence defined in terms of probability distributions, for a classical propositional language using a standard notion of probability. We examine…
HYBRINFOX at CheckThat! 2024 -- Task 1: Enhancing Language Models with Structured Information for Check-Worthiness Estimation
Géraud Faye, Morgane Casanova, Benjamin Icard +4
This paper summarizes the experiments and results of the HYBRINFOX team for the CheckThat! 2024 - Task 1 competition. We propose an approach enriching Language Models such as RoBER…
HYBRINFOX at CheckThat! 2024 -- Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection
Morgane Casanova, Julien Chanson, Benjamin Icard +4
This paper presents the HYBRINFOX method used to solve Task 2 of Subjectivity detection of the CLEF 2024 CheckThat! competition. The specificity of the method is to use a hybrid sy…