Enhancing Semantic Similarity Understanding in Arabic NLP with Nested
  Embedding Learning
Authors: name: Omer Nacar, name: Anis Koubaa

This work presents a novel framework for training Arabic nested embedding
models through Matryoshka Embedding Learning, leveraging multilingual,
Arabic-specific, and English-based models, to highlight the power of nested
embeddings models in various Arabic NLP downstream tasks. Our innovative
contribution includes the translation of various sentence similarity datasets
into Arabic, enabling a comprehensive evaluation framework to compare these
models across different dimensions. We trained several nested embedding models
on the Arabic Natural Language Inference triplet dataset and assessed their
performance using multiple evaluation metrics, including Pearson and Spearman
correlations for cosine similarity, Manhattan distance, Euclidean distance, and
dot product similarity. The results demonstrate the superior performance of the
Matryoshka embedding models, particularly in capturing semantic nuances unique
to the Arabic language. Results demonstrated that Arabic Matryoshka embedding
models have superior performance in capturing semantic nuances unique to the
Arabic language, significantly outperforming traditional models by up to
20-25\% across various similarity metrics. These results underscore the
effectiveness of language-specific training and highlight the potential of
Matryoshka models in enhancing semantic textual similarity tasks for Arabic
NLP.