HYBRINFOX at CheckThat! 2024 – Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2024
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| _version_ | 1866901802237034496 |
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| author | Casanova, Morgane Chanson, Julien Icard, Benjamin Faye, Géraud Gadek, Guillaume Gravier, Guillaume Égré, Paul |
| author_facet | Casanova, Morgane Chanson, Julien Icard, Benjamin Faye, Géraud Gadek, Guillaume Gravier, Guillaume Égré, Paul |
| contents | <p>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, f ine-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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14857651 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
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| spellingShingle | HYBRINFOX at CheckThat! 2024 – Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection Casanova, Morgane Chanson, Julien Icard, Benjamin Faye, Géraud Gadek, Guillaume Gravier, Guillaume Égré, Paul Subjectivity Objectivity Vagueness Hybrid AI VAGO Large Language Models <p>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, f ine-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.</p> |
| title | HYBRINFOX at CheckThat! 2024 – Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection |
| topic | Subjectivity Objectivity Vagueness Hybrid AI VAGO Large Language Models |
| url | https://doi.org/10.5281/zenodo.14857651 |