Aymé Arango

Also published as: Ayme Arango


2022

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HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification
Ayme Arango | Jesus Perez-Martin | Arniel Labrada
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has been conducted almost exclusively over textual content, and the analysis of pictures and videos has been very scarce. This paper describes our team proposal in the Multimedia Automatic Misogyny Identification (MAMI) task at SemEval 2022. The challenge consisted of identifying misogynous memes from a dataset where images and text transcriptions were provided. We reported a 71% of F-score using a multimodal system based on the CLIP model.

2020

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ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection
Juan Manuel Pérez | Aymé Arango | Franco Luque
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research