Bilingual Sexism Classification: Fine-Tuned XLM-RoBERTa and GPT-3.5 Few-Shot Learning

TL;DR

This paper explores bilingual sexism classification using fine-tuned XLM-RoBERTa and GPT-3.5 few-shot learning to improve detection accuracy in English and Spanish online content, addressing online harassment issues.

Contribution

It introduces a dual approach combining fine-tuned XLM-RoBERTa and GPT-3.5 few-shot learning for bilingual sexism detection, achieving competitive results in a challenge setting.

Findings

XLM-RoBERTa achieved 4th place in sexism identification.

GPT-3.5 demonstrated effective few-shot learning capabilities.

The methods improved bilingual sexism classification performance.

Abstract

Sexism in online content is a pervasive issue that necessitates effective classification techniques to mitigate its harmful impact. Online platforms often have sexist comments and posts that create a hostile environment, especially for women and minority groups. This content not only spreads harmful stereotypes but also causes emotional harm. Reliable methods are essential to find and remove sexist content, making online spaces safer and more welcoming. Therefore, the sEXism Identification in Social neTworks (EXIST) challenge addresses this issue at CLEF 2024. This study aims to improve sexism identification in bilingual contexts (English and Spanish) by leveraging natural language processing models. The tasks are to determine whether a text is sexist and what the source intention behind it is. We fine-tuned the XLM-RoBERTa model and separately used GPT-3.5 with few-shot learning prompts to classify sexist content. The XLM-RoBERTa model exhibited robust performance in handling complex linguistic structures, while GPT-3.5's few-shot learning capability allowed for rapid adaptation to new data with minimal labeled examples. Our approach using XLM-RoBERTa achieved 4th place in the soft-soft evaluation of Task 1 (sexism identification). For Task 2 (source intention), we achieved 2nd place in the soft-soft evaluation.