The task consists in determining whether a sentence contains at least one stereotype or none and assigning those sentences previously marked as positive (with stereotypes) to at least one of the ten categories that present immigrants as: 1) ‘victims of xenophobia’, 2) ‘suffering victims’, 3) ‘economic resources’, 4) a problem of ‘migration control’, 5) people with ‘cultural and religious differences’, 6) people that take advantage of welfare ‘benefits’, 7) a problem for ‘public health’, 8) a threat to ‘security’, 9) ‘dehumanization’ and 10) ‘other’ types of stereotypes.
It is a multi-label classification problem, since each instance may be assigned one or more categories.
Task results
System | Precision | 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 Sort ascending | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 | WAC | b2 | erde30 | sent | weighted f1 |
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MALNIS | 0.88 | -0.24 | 0.87 | ||||||||||||||||||||||||||||||||||
UMUTeam | 0.88 | -0.33 | 0.87 | ||||||||||||||||||||||||||||||||||
Lak NLP | 0.86 | -0.42 | 0.85 |