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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ABAEMWDQV" target="_blank" >RIV/00216208:11320/25:BAEMWDQV - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205299390&partnerID=40&md5=3c08dc1ec6bb453a7ff93383c6574842" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205299390&partnerID=40&md5=3c08dc1ec6bb453a7ff93383c6574842</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 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. © 2024 Association for Computational Linguistics.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 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. © 2024 Association for Computational Linguistics.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proc. Annu. Meet. Assoc. Comput Linguist.

ISBN

979-889176099-8

ISSN

0736-587X

e-ISSN

—

Počet stran výsledku

15

Strana od-do

10103-10117

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

—

Místo konání akce

Hybrid, Bangkok

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—