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Overview of DETESTS-Dis at IberLEF 2024: DETEction and classification of racial STereotypes in Spanish - Learning with Disagreement

  • Wolfgang S. Schmeisser-Nieto
  • , Pol Pastells
  • , Simona Frenda
  • , Alejandro Ariza-Casabona
  • , Mireia Farrús
  • , Paolo Rosso
  • , Mariona Taulé

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper presents an overview of the DETESTS-Dis shared task as part of the IberLEF 2024 Workshop on Iberian Languages Evaluation Forum, within the framework of the SEPLN 2024 conference. We proposed two hierarchical subtasks: In subtask 1, participants had to determine the presence of stereotypes in the texts. For subtask 2, participants had to decide which texts labeled with stereotypes were implicit stereotypes. The DETESTS-Dis dataset contains 12,111 comment sentences and tweets in response to newspaper articles and verified racial hoaxes involving immigration in Spanish. 15 teams signed up to participate, 6 of which sent runs, and 3 of them sent their working notes. In this paper, we provide information about the training and test datasets, the systems used by the participants, the evaluation metrics of the systems and their results.
Original languageEnglish
Pages (from-to)323-333
Number of pages11
JournalProcesamiento del Lenguaje Natural
Volume73
DOIs
Publication statusPublished - 21 Sept 2024
EventIberian Languages Evaluation Forum 2024 - Valladolid, Spain
Duration: 24 Sept 2024 → 24 Sept 2024
https://ceur-ws.org/Vol-3756/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Keywords

  • Immigration
  • Implicitness Detection
  • Machine Learning
  • Stereotype Detection

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Computer Science Applications

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