Multiclass problem that consists in categorize the message according to the type of sexism it encloses.
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
F1
Task results
System | Precision | Recall | F1 Sort ascending | CEM | Accuracy | MacroPrecision | MacroRecall | MacroF1 | RMSE | MicroPrecision | MicroRecall | MicroF1 | MAE | MAP | UAS | LAS | MLAS | BLEX | Pearson correlation | Spearman correlation | MeasureC | BERTScore | EMR | Exact Match | F0.5 | Hierarchical F | ICM | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 | WAC | b2 | erde30 | sent | weighted f1 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Roberta large | 0.5846 | 0.5846 | 0.5846 | 0.5846 | 0.58 | ||||||||||||||||||||||||||||||||
Distilbert base uncased | 0.5486 | 0.5486 | 0.5486 | 0.5486 | 0.55 | ||||||||||||||||||||||||||||||||
Xlm roberta large | 0.5422 | 0.5422 | 0.5422 | 0.5422 | 0.54 | ||||||||||||||||||||||||||||||||
Xlm roberta base | 0.5345 | 0.5345 | 0.5345 | 0.5345 | 0.53 | ||||||||||||||||||||||||||||||||
Bert base cased | 0.5344 | 0.5344 | 0.5344 | 0.5344 | 0.53 | ||||||||||||||||||||||||||||||||
Ixa ehu ixambert base cased | 0.5300 | 0.5300 | 0.5300 | 0.5300 | 0.53 | ||||||||||||||||||||||||||||||||
Roberta base | 0.5258 | 0.5258 | 0.5258 | 0.5258 | 0.53 | ||||||||||||||||||||||||||||||||
Bert base multilingual cased | 0.5022 | 0.5022 | 0.5022 | 0.5022 | 0.50 | ||||||||||||||||||||||||||||||||
Distilbert base multilingual cased | 0.4792 | 0.4792 | 0.4792 | 0.4792 | 0.48 |