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The recent advances in natural language processing have predominantly favoredwell-resourced English-centric models, resulting in a significant gap withlow-resource languages. In this work, we introduce the language model TURNA,which is developed for the low-resource language Turkish and is capable of bothnatural language understanding and generation tasks. TURNA is pretrained withan encoder-decoder architecture based on the unified framework UL2 with adiverse corpus that we specifically curated for this purpose. We evaluatedTURNA with three generation tasks and five understanding tasks for Turkish. Theresults show that TURNA outperforms several multilingual models in bothunderstanding and generation tasks, and competes with monolingual Turkishmodels in understanding tasks. TURNA is made available athttps://huggingface.co/boun-tabi-LMG/TURNA .

TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation