Binary classification problem, consisting in determmine whether a text or message is sexist or not. It includes any type of sexist expression or related phenomena, like descriptive or reported assertions where the sexist message is a report or a description of a sexist event. In particular, we consider two labels:
- Sexist: the tweet or gab expresses sexist behaviours or discourses.
- Non-Sexist: the tweet or gab does not express any sexist behaviour or discourse.
Publication
Francisco Rodríguez-Sánchez, Jorge Carrillo-de-Albornoz, Laura Plaza, Adrián Mendieta-Aragón, Guillermo Marco-Remón, Maryna Makeienko, María Plaza, Julio Gonzalo, Damiano Spina, Paolo Rosso (2022) Overview of EXIST 2022: sEXism Identification in Social neTworks. Procesamiento del Lenguaje Natural, Revista nº 69, septiembre de 2022, pp. 229-240.
Language
English
URL Task
NLP topic
Abstract task
Dataset
Year
2022
Publication link
Ranking metric
Accuracy
Task results
| System | F1 Sort ascending | Accuracy | MacroF1 | Pearson correlation | ICM |
|---|---|---|---|---|---|
| Roberta large | 0.8187 | 0.8187 | 0.8187 | 0.8187 | 0.82 |
| Xlm roberta large | 0.7953 | 0.7953 | 0.7953 | 0.7953 | 0.80 |
| Roberta base | 0.7875 | 0.7875 | 0.7875 | 0.7875 | 0.79 |
| Distilbert base uncased | 0.7739 | 0.7739 | 0.7739 | 0.7739 | 0.77 |
| Xlm roberta base | 0.7661 | 0.7661 | 0.7661 | 0.7661 | 0.77 |
| Bert base cased | 0.7641 | 0.7641 | 0.7641 | 0.7641 | 0.76 |
| Bert base multilingual cased | 0.7563 | 0.7563 | 0.7563 | 0.7563 | 0.76 |
| Ixa ehu ixambert base cased | 0.7563 | 0.7563 | 0.7563 | 0.7563 | 0.76 |
| Distilbert base multilingual cased | 0.7388 | 0.7388 | 0.7388 | 0.7388 | 0.74 |
