HYBRINFOX at CheckThat! 2024 -- Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection

TL;DR

This paper introduces HYBRINFOX, a hybrid approach combining BERT, sentence-BERT, and expert system scores for subjectivity detection, achieving top results in English and mixed results in multilingual settings.

Contribution

The paper presents a novel hybrid system integrating deep learning models with expert system scores for improved subjectivity detection, especially in English.

Findings

Ranked 1st in English with macro F1 of 0.7442

Effective in multilingual English translation approach

Hybrid system outperforms baseline in English and some languages

Abstract

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 system, combining a RoBERTa model, fine-tuned for subjectivity detection, a frozen sentence-BERT (sBERT) model to capture semantics, and several scores calculated by the English version of the expert system VAGO, developed independently of this task to measure vagueness and subjectivity in texts based on the lexicon. In English, the HYBRINFOX method ranked 1st with a macro F1 score of 0.7442 on the evaluation data. For the other languages, the method used a translation step into English, producing more mixed results (ranking 1st in Multilingual and 2nd in Italian over the baseline, but under the baseline in Bulgarian, German, and Arabic). We explain the principles of our hybrid approach, and outline ways in which the method could be improved for other languages besides English.