A Benchmark Study of Contrastive Learning for Arabic Social Meaning

TL;DR

This paper conducts a comprehensive benchmark study of contrastive learning methods applied to Arabic social meaning NLP tasks, demonstrating their effectiveness and data efficiency, especially in low-resource settings.

Contribution

It is the first extensive evaluation of contrastive learning for Arabic social meaning tasks, showing its advantages over traditional finetuning methods.

Findings

CL outperforms vanilla finetuning on most tasks

CL is data efficient, especially in low-resource scenarios

The study provides empirical evidence of CL's promise for Arabic NLP

Abstract

Contrastive learning (CL) brought significant progress to various NLP tasks. Despite this progress, CL has not been applied to Arabic NLP to date. Nor is it clear how much benefits it could bring to particular classes of tasks such as those involved in Arabic social meaning (e.g., sentiment analysis, dialect identification, hate speech detection). In this work, we present a comprehensive benchmark study of state-of-the-art supervised CL methods on a wide array of Arabic social meaning tasks. Through extensive empirical analyses, we show that CL methods outperform vanilla finetuning on most tasks we consider. We also show that CL can be data efficient and quantify this efficiency. Overall, our work allows us to demonstrate the promise of CL methods, including in low-resource settings.