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