A Benchmark Study of Contrastive Learning for Arabic Social Meaning
This work addresses the problem of improving NLP performance for Arabic social meaning tasks, but it is incremental as it applies existing CL methods to a new domain.
The authors tackled the lack of contrastive learning applications in Arabic NLP by benchmarking state-of-the-art supervised CL methods on various Arabic social meaning tasks, showing that CL outperforms vanilla fine-tuning on most tasks and is data-efficient.
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.