Modelos Basados en Enmascaramiento y en BERT para la Identificación de Estereotipos

  1. Montes y Gómez, Manuel
  2. Chulvi, Berta
  3. Sánchez-Junquera, Javier
  4. Rosso, Paolo
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2021

Número: 67

Páginas: 83-94

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

Los estereotipos sobre inmigrantes son un tipo de sesgo social cada vez más presente en la interacción humana en redes sociales y en los discursos políticos. Esta desafiante tarea está siendo estudiada por la lingüística computacional debido al aumento de los mensajes de odio, el lenguaje ofensivo, y la discriminación que reciben muchas personas. En este trabajo, nos proponemos identificar estereotipos sobre inmigrantes utilizando dos enfoques diametralmente opuestos prestando atención a la explicabilidad de los mismos: un modelo de aprendizaje profundo basado en Transformers; y una técnica de enmascaramiento de texto que ha sido reconocida por su capacidad para ofrecer buenos resultados a la vez que comprensibles para los humanos. Finalmente, mostramos la idoneidad de los dos modelos para la tarea, y ofrecemos algunos ejemplos de sus ventajas en términos de explicabilidad.

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Fuente de los datos: Dialnet