EXIST 2022: Sexism detection

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.
Publicación
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.
Competición
EXIST 2022: sEXism Identification in Social neTworks
Idioma
Inglés
URL Tarea
http://nlp.uned.es/exist2022/
NLP topic
detección de odio
Tarea abstracta
Clasificación
Dataset
EXIST-2022-ES
Año
2022
Enlace publicación
http://journal.sepln.org/sepl…
Métrica Ranking
Accuracy

Mejores resultados para la tarea

Sistema Precisión Recall F1 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
avacaondata_1 0.8454 0.8454 0.8454 0.8500 0.52
avacaondata_3 0.8454 0.8454 0.8454 0.8500 0.52
SINAI-TL_1 0.8179 0.8252 0.8200 0.8231 0.45
SINAI-TL_3 0.8173 0.8224 0.8192 0.8231 0.44
I2C_1 0.8161 0.8253 0.8171 0.8192 0.44
AI-UPV_3 0.8140 0.8212 0.8161 0.8192 0.43
CIMATCOLMEX_3 0.8116 0.8175 0.8136 0.8173 0.43
CIMATCOLMEX_2 0.8060 0.8126 0.8081 0.8115 0.41
multiaztertest_2 0.8024 0.7997 0.8009 0.8077 0.39
ELiRF-VRAIN_3 0.7956 0.7975 0.7965 0.8019 0.37