Transformer Ensembles for Sexism Detection

• URL: http://arxiv.org/abs/2110.15905v1
• Date: Fri, 29 Oct 2021 16:51:50 GMT
• Title: Transformer Ensembles for Sexism Detection
• Authors: Lily Davies, Marta Baldracchi, Carlo Alessandro Borella, and Konstantinos Perifanos
• Abstract summary: This document presents in detail the work done for the sexism detection task at EXIST2021 workshop. Our methodology is built on ensembles of Transformer-based models which are trained on different background and corpora. We report accuracy of 0.767 for the binary classification task (task1), and f1 score 0.766, and for the multi-class task (task2) accuracy 0.623 and f1-score 0.535.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This document presents in detail the work done for the sexism detection task at EXIST2021 workshop. Our methodology is built on ensembles of Transformer-based models which are trained on different background and corpora and fine-tuned on the provided dataset from the EXIST2021 workshop. We report accuracy of 0.767 for the binary classification task (task1), and f1 score 0.766, and for the multi-class task (task2) accuracy 0.623 and f1-score 0.535.

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