title: Enhancing Semantic Similarity Understanding in Arabic NLP with Nested
    Embedding Learning
summary: 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.
paper_authors._id: 66acc7c7828a67c0148e2108
paper_authors.name: Omer Nacar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-02T12:36:18.758Z
paper_authors._id: 628f7a71dd993507cfcbe587
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/628f7a71dd993507cfcbe587/3frAcMgfBCx-OJsrdAhnb.png
paper_authors.fullname: Omartificial Intelligence Space
paper_authors.type: user
paper_authors.user: Omartificial-Intelligence-Space
paper_authors._id: 66acc7c7828a67c0148e2109
paper_authors.name: Anis Koubaa
paper_title: Enhancing Semantic Similarity Understanding in Arabic NLP with Nested
    Embedding Learning
paper_summary: 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.
paper_ai_summary: Matryoshka Embedding Learning framework enhances Arabic NLP tasks, particularly semantic similarity, by leveraging nested embeddings and multilingual models.
paper_ai_keywords: nested embedding models Matryoshka Embedding Learning sentence similarity datasets Arabic Natural Language Inference cosine similarity Manhattan distance Euclidean distance dot product similarity semantic nuances