UL &amp; UM6P at SemEval-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism

Lamsiyah, Salima; Mahdaouy, Abdelkader El; Alami, Hamza; Berrada, Ismaïl; Schommer, Christoph

doi:10.18653/v1/2023.semeval-1.88

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…However, along with this positive shift, a significant amount of false, misleading, and harmful content has also emerged. This includes various forms of problematic content like misinformation, disinformation, and malinformation in the form of spreading propaganda, conspiracy theories, rumors, hoaxes, fake news, false statements, hate speech, cyberbullying, and among others (Oshikawa et al, 2020;Sharara et al, 2022;Essefar et al, 2021;Nakov et al, 2021a;Alam et al, 2022;Lamsiyah et al, 2023;Mubarak et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

UL & UM6P at ArAIEval Shared Task: Transformer-based model for Persuasion Techniques and Disinformation detection in Arabic

Lamsiyah,

Mahdaouy,

Alami

et al. 2023

Proceedings of ArabicNLP 2023

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In this paper, we introduce our participating system to the ArAIEval Shared Task, addressing both the detection of persuasion techniques and disinformation tasks. Our proposed system employs a pre-trained transformer-based language model for Arabic, alongside a classifier. We have assessed the performance of three Arabic Pre-trained Language Models (PLMs) for sentence encoding. Additionally, to enhance our model's performance, we have explored various training objectives, including Cross-Entropy loss, regularized Mixup loss, asymmetric multi-label loss, and Focal Tversky loss. On the official test set, our system has achieved micro-F1 scores of 0.7515, 0.5666, 0.904, and 0.8333 for Sub-Task 1A, Sub-Task 1B, Sub-Task 2A, and Sub-Task 2B, respectively. Furthermore, our system has secured the 4th, 1st, 3rd, and 2nd positions, respectively, among all participating systems in sub-tasks 1A, 1B, 2A, and 2B of the ArAIEval shared task.

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Section: Introductionmentioning

confidence: 99%

UL & UM6P at ArAIEval Shared Task: Transformer-based model for Persuasion Techniques and Disinformation detection in Arabic

Lamsiyah,

Mahdaouy,

Alami

et al. 2023

Proceedings of ArabicNLP 2023

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A Transparent Pipeline for Identifying Sexism in Social Media: Combining Explainability with Model Prediction

Mohammadi,

Giachanou,

Bagheri

2024

Applied Sciences

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In this study, we present a new approach that combines multiple Bidirectional Encoder Representations from Transformers (BERT) architectures with a Convolutional Neural Network (CNN) framework designed for sexism detection in text at a granular level. Our method relies on the analysis and identification of the most important terms contributing to sexist content using Shapley Additive Explanations (SHAP) values. This approach involves defining a range of Sexism Scores based on both model predictions and explainability, moving beyond binary classification to provide a deeper understanding of the sexism-detection process. Additionally, it enables us to identify specific parts of a sentence and their respective contributions to this range, which can be valuable for decision makers and future research. In conclusion, this study introduces an innovative method for enhancing the clarity of large language models (LLMs), which is particularly relevant in sensitive domains such as sexism detection. The incorporation of explainability into the model represents a significant advancement in this field. The objective of our study is to bridge the gap between advanced technology and human comprehension by providing a framework for creating AI models that are both efficient and transparent. This approach could serve as a pipeline for future studies to incorporate explainability into language models.

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UL & UM6P at SemEval-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism

Cited by 2 publications

References 29 publications

UL & UM6P at ArAIEval Shared Task: Transformer-based model for Persuasion Techniques and Disinformation detection in Arabic

UL & UM6P at ArAIEval Shared Task: Transformer-based model for Persuasion Techniques and Disinformation detection in Arabic

A Transparent Pipeline for Identifying Sexism in Social Media: Combining Explainability with Model Prediction

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