Masking and BERT-based Models for Stereotype Identication
[EN] Stereotypes about immigrants are a type of social bias increasingly present in the human interaction in social networks and political speeches. This challenging task is being studied by computational linguistics because of the rise of hate messages, offensive language, and discrimination that m...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/183769 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/183769 |
| Access Level: | acceso abierto |
| Palabra clave: | Social bias Immigrant stereotypes BETO Masking technique Sesgo social Estereotipos hacia inmigrantes Técnica de enmascaramiento LENGUAJES Y SISTEMAS INFORMATICOS |
| Sumario: | [EN] Stereotypes about immigrants are a type of social bias increasingly present in the human interaction in social networks and political speeches. This challenging task is being studied by computational linguistics because of the rise of hate messages, offensive language, and discrimination that many people receive. In this work, we propose to identify stereotypes about immigrants using two different explainable approaches: a deep learning model based on Transformers; and a text masking technique that has been recognized by its capabilities to deliver good and human-understandable results. Finally, we show the suitability of the two models for the task and offer some examples of their advantages in terms of explainability |
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