GATE: General Arabic Text Embedding for Enhanced Semantic Textual
  Similarity with Matryoshka Representation Learning and Hybrid Loss Training
Authors: name: Omer Nacar, name: Anis Koubaa, name: Serry Sibaee, name: Yasser Al-Habashi, name: Adel Ammar, name: Wadii Boulila

Semantic textual similarity (STS) is a critical task in natural language
processing (NLP), enabling applications in retrieval, clustering, and
understanding semantic relationships between texts. However, research in this
area for the Arabic language remains limited due to the lack of high-quality
datasets and pre-trained models. This scarcity of resources has restricted the
accurate evaluation and advance of semantic similarity in Arabic text. This
paper introduces General Arabic Text Embedding (GATE) models that achieve
state-of-the-art performance on the Semantic Textual Similarity task within the
MTEB benchmark. GATE leverages Matryoshka Representation Learning and a hybrid
loss training approach with Arabic triplet datasets for Natural Language
Inference, which are essential for enhancing model performance in tasks that
demand fine-grained semantic understanding. GATE outperforms larger models,
including OpenAI, with a 20-25% performance improvement on STS benchmarks,
effectively capturing the unique semantic nuances of Arabic.