When Multiple Perspectives and an Optimization Process Lead to Better Performance, an Automatic Sexism Identification on Social Media With Pretrained Transformers in a Soft Label Context
Résumé
Even if today, the sexism is socially widely disapproving, it remains an omnipresent phenomenon in our society. But faced with huge quantities of data, social platforms are struggling to identify it. This highlights the need to develop automatic detection tools that can subtly assess the sexistness of usergenerated content. That's what sEXism Identification in Social neTworks (EXIST) is all about. The EXIST 2023 contest consists of three classification tasks : 1. detect sexism, 2. clarify the author's intention and 3. explicit the sexism type. Thanks to these three tasks, each data could be seen from three different points of view. This idea, combined with fine-tuned BERTs, model stacking and an optimization process, enabled us to rank 1 st in the task 2 and 4 th in the task 3 in a soft label context. This paper describes our approach, our negative results and some possible perspectives.
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