The recent advances in natural language processing have predominantly favored well-resourced English-centric models, resulting in a significant gap with low-resource languages. In this work, we introduce the language model TURNA, which is developed for the low-resource language Turkish and is capable of both natural language understanding and generation tasks. TURNA is pretrained with an encoder-decoder architecture based on the unified framework UL2 with a diverse corpus that we specifically curated for this purpose. We evaluated TURNA with three generation tasks and five understanding tasks for Turkish. The results show that TURNA outperforms several multilingual models in both understanding and generation tasks, and competes with monolingual Turkish models in understanding tasks. TURNA is made available at https://huggingface.co/boun-tabi-LMG/TURNA .

最近在自然语言处理方面的进展主要偏向于资源丰富的英语模型，导致与资源稀缺的语言存在显著差距。本研究引入了专为资源稀缺的土耳其语而开发的语言模型TURNA，该模型能够进行自然语言理解和生成任务。我们使用面向此目的精心策划的多样语料库，在统一框架UL2的编码器-解码器架构下对TURNA进行预训练。我们针对土耳其语评估了TURNA 在三项生成任务和五项理解任务中的表现。结果表明，TURNA在理解和生成任务上胜过多个多语言模型，并与单语土耳其模型在理解任务上竞争力相当。TURNA已在此URL链接上提供。

TURNA：一种用于增强理解和生成的土耳其编码器-解码器语言模型