Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning

Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning

In a groundbreaking study, Omer Nacar present a novel framework for training Arabic nested embedding models via Matryoshka Embedding Learning. This approach leverages multilingual, Arabic-specific, and English-based models to highlight the potential of nested embeddings in various Arabic NLP downstream tasks. A key innovative contribution of this work is the translation of several sentence similarity datasets into Arabic, allowing a comprehensive evaluation of these models across different dimensions.

The author trained multiple nested embedding models on the Arabic Natural Language Inference (NLI) triplet dataset, assessing their performance using various evaluation metrics such as Pearson and Spearman correlations for cosine similarity, Manhattan distance, Euclidean distance, and dot product similarity. The results showcase the superior performance of the Matryoshka embedding models, particularly in capturing semantic nuances unique to the Arabic language. These models outperformed traditional models by up to 20-25% across various similarity metrics, underscoring the effectiveness of language-specific training and the potential of Matryoshka models in enhancing semantic textual similarity tasks for Arabic NLP.

Key Contributions:

• Development of Arabic NLI Datasets:
  Translation of the English Stanford Natural Language Inference (SNLI) and MultiNLI datasets into Arabic using neural machine translation (NMT), providing critical resources for Arabic natural language inference tasks.

• Training of Matryoshka Embedding Models:
  Transformation of various English and Arabic embedding models into Matryoshka versions, enhancing their adaptability and performance across different tasks.

• Comprehensive Evaluation and Public Release:
  Extensive evaluation of these trained models, offering valuable insights and making both the datasets and models publicly available on Hugging Face to facilitate broader research and application.

Detailed Discussion:

• Matryoshka Representation Learning (MRL): This paper delves into the core principles of Matryoshka Representation Learning, highlighting its ability to create adaptable, nested representations through explicit optimization. This methodology is crucial for large-scale classification and retrieval tasks, providing significant computational benefits without compromising accuracy. The practical advantages of MRL are demonstrated by integrating it with established NLP models, achieving notable speed-ups and maintaining high accuracy across various applications.

• Performance Evaluation: The Matryoshka embedding models were evaluated on multiple metrics, including Pearson and Spearman correlations for cosine similarity, Manhattan distance, Euclidean distance, and dot product similarity. The results demonstrated the models' superior performance in capturing semantic nuances unique to the Arabic language, with a significant improvement of 20-25% over traditional models across various similarity metrics.

• Public Release: To promote further research and application, the author has made the datasets and models publicly available on Hugging Face, providing the NLP community with valuable resources for Arabic natural language processing tasks.

Link to Models collection : https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e
Link to Dataset collection: https://huggingface.co/collections/Omartificial-Intelligence-Space/arabic-nli-and-semantic-similarity-datasets-6671ba0a5e4cd3f5caca50c3