Automatic Sexism Detection with Multilingual Transformer Models

• URL: http://arxiv.org/abs/2106.04908v1
• Date: Wed, 9 Jun 2021 08:45:51 GMT
• Title: Automatic Sexism Detection with Multilingual Transformer Models
• Authors: Sch\"utz Mina, Boeck Jaqueline, Liakhovets Daria, Slijep\v{c}evi\'c Djordje, Kirchknopf Armin, Hecht Manuel, Bogensperger Johannes, Schlarb Sven, Schindler Alexander, Zeppelzauer Matthias
• Abstract summary: This paper presents the contribution of the AIT_FHSTP team at the EXIST 2021 benchmark for two sEXism Identification in Social neTworks tasks. To solve the tasks we applied two multilingual transformer models, one based on multilingual BERT and one based on XLM-R. Our approach uses two different strategies to adapt the transformers to the detection of sexist content: first, unsupervised pre-training with additional data and second, supervised fine-tuning with additional and augmented data. For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sexism has become an increasingly major problem on social networks during the last years. The first shared task on sEXism Identification in Social neTworks (EXIST) at IberLEF 2021 is an international competition in the field of Natural Language Processing (NLP) with the aim to automatically identify sexism in social media content by applying machine learning methods. Thereby sexism detection is formulated as a coarse (binary) classification problem and a fine-grained classification task that distinguishes multiple types of sexist content (e.g., dominance, stereotyping, and objectification). This paper presents the contribution of the AIT_FHSTP team at the EXIST2021 benchmark for both tasks. To solve the tasks we applied two multilingual transformer models, one based on multilingual BERT and one based on XLM-R. Our approach uses two different strategies to adapt the transformers to the detection of sexist content: first, unsupervised pre-training with additional data and second, supervised fine-tuning with additional and augmented data. For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets and fine-tuning on the provided dataset. The best run for the binary classification (task 1) achieves a macro F1-score of 0.7752 and scores 5th rank in the benchmark; for the multiclass classification (task 2) our best submission scores 6th rank with a macro F1-score of 0.5589.

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