@inproceedings{uludogan-etal-2024-turna,
title = "{TURNA}: A {T}urkish Encoder-Decoder Language Model for Enhanced Understanding and Generation",
author = {Uludo{\u{g}}an, G{\"o}k{\c{c}}e and
Balal, Zeynep and
Akkurt, Furkan and
Turker, Meliksah and
Gungor, Onur and
{\"U}sk{\"u}darl{\i}, Susan},
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.600",
pages = "10103--10117",
abstract = "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 TURNA, a language model 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.",
}
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<abstract>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 TURNA, a language model 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.</abstract>
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%0 Conference Proceedings
%T TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation
%A Uludoğan, Gökçe
%A Balal, Zeynep
%A Akkurt, Furkan
%A Turker, Meliksah
%A Gungor, Onur
%A Üsküdarlı, Susan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F uludogan-etal-2024-turna
%X 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 TURNA, a language model 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.
%U https://aclanthology.org/2024.findings-acl.600
%P 10103-10117
Markdown (Informal)
[TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation](https://aclanthology.org/2024.findings-acl.600) (Uludoğan et al., Findings 2024)
ACL