Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning

Summary

This paper introduces a nested embedding learning framework for Arabic NLP, utilizing Matryoshka Embedding Learning and multilingual models. The authors translated sentence similarity datasets into Arabic to enable comprehensive evaluation. Experiments on the Arabic Natural Language Inference dataset show Matryoshka embedding models outperform traditional models by 20-25% in capturing Arabic semantic nuances. Why it matters: This work advances Arabic NLP by providing a new method and evaluation benchmark for semantic similarity, which is crucial for tasks like information retrieval and text understanding.