Fiche du document
2020
- 20.500.13089/1dis
Ce document est lié à :
https://hdl.handle.net/20.500.13089/1cho
Ce document est lié à :
https://doi.org/10.4000/books.aaccademia
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/isbn/979-12-80136-32-9
info:eu-repo/semantics/openAccess , https://creativecommons.org/licenses/by-nc-nd/4.0/
Résumé
Stance detection refers to the detection of one’s opinion about the target from their statements. The aim of sardistance task is to classify the Italian tweets into classes of favor, against or no feeling towards the target. The task has two sub-tasks : in Task A, the classification has to be done by considering only the textual meaning whereas in Task B the tweets must be classified by considering the contextual information along with the textual meaning. We have presented our solution to detect the stance utilizing only the textual meaning (Task A) using encoder-decoder model and transformers. Among these two approaches, simple transformers have performed better than the encoder-decoder model with an average F1-score of 0.4707.
