Sexism identification

This is a multi-class classification tasks. The systems have to decide whether or not a given tweet contains sexist expressions or behaviours (i.e., it is sexist itself, describes a sexist situation or criticizes a sexist behaviour).

Publication
Francisco Rodríguez-Sánchez, Jorge Carrillo-de-Albornoz, Laura Plaza, Julio Gonzalo, Paolo Rosso, Miriam Comet, Trinidad Donoso. Overview of EXIST 2021: sEXism Identification in Social neTworks.. Procesamiento del Lenguaje Natural, Vol 67, septiembre 2021.
Language
Spanish
English
NLP topic
Abstract task
Dataset
Year
2021
Ranking metric
Accuracy

Task results

System Accuracy Sort ascending MacroPrecision MacroRecall MacroF1
AI_UPV_1 0.7804 0.7801 0.7806 0.7802
SINAI_TL_1 0.7800 0.7796 0.7800 0.7797
AIT_FHSTP_2 0.7754 0.7751 0.7756 0.7752
multiaztertest_1 0.7740 0.7741 0.7727 0.7731
nlp_unes_team_1 0.7720 0.7720 0.7737 0.7696

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.