arXiv:2606.03027v1 Announce Type: new Abstract: Text embeddings are fundamental to many downstream applications, making robustness important for real-world NLP. However, most recent state-of-the-art embedding models are not reproducible because they rely on closed or undisclosed training data, and they remain insufficiently robust for Southeast Asian languages. We present SEA-Embedding, a fully open and reproducible text-embedding pipeline for Southeast Asian languages trained only on publicly available data, and use it to study three core factors of robust embedding design: data composition,
The increasing reliance on AI for various applications, coupled with geopolitical tensions, is driving a push for localized and reproducible AI infrastructure, especially in regions with diverse linguistic landscapes like Southeast Asia.
This initiative provides a replicable methodology for developing foundational AI models specifically tailored for Southeast Asian languages, reducing dependence on Western or Chinese-centric solutions and fostering regional AI capabilities.
The availability of open and reproducible text embeddings for Southeast Asian languages enables localized AI development without proprietary data dependencies, potentially accelerating innovation and adoption within the region.
- · Southeast Asian AI developers
- · Regional tech companies
- · Academic researchers
- · Governments in Southeast Asia
- · Proprietary model providers with limited regional focus
- · AI companies reliant on closed datasets
Increased development and deployment of NLP applications specifically for Southeast Asian languages.
Reduced linguistic data colonialism and greater digital sovereignty for nations in the region.
Enhanced economic competitiveness and cultural preservation through AI tailored to local contexts, potentially leading to new regional AI power blocs.
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Read at arXiv cs.CL