Enhancing Turkish Coreference Resolution: Insights from deep learning, dropped pronouns, and multilingual transfer learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AURUAKRCC" target="_blank" >RIV/00216208:11320/26:URUAKRCC - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196493074&doi=10.1016%2fj.csl.2024.101681&partnerID=40&md5=bf81abd209578fe1945668d533bae5bc" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196493074&doi=10.1016%2fj.csl.2024.101681&partnerID=40&md5=bf81abd209578fe1945668d533bae5bc</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csl.2024.101681" target="_blank" >10.1016/j.csl.2024.101681</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Turkish Coreference Resolution: Insights from deep learning, dropped pronouns, and multilingual transfer learning

Popis výsledku v původním jazyce

Coreference resolution (CR), which is the identification of in-text mentions that refer to the same entity, is a crucial step in natural language understanding. While CR in English has been studied for quite a long time, studies for pro-dropped and morphologically rich languages is an active research area which has yet to reach sufficient maturity. Turkish, a morphologically highly-rich language, poses interesting challenges for natural language processing tasks, including CR, due to its agglutinative nature and consequent pronoun-dropping phenomenon. This article explores the use of different neural CR architectures (i.e., mention-pair, mention-ranking, and end-to-end) on Turkish, a morphologically highly-rich language, by formulating multiple research questions around the impacts of dropped pronouns, data quality, and interlingual transfer. The preparations made to explore these research questions and the findings obtained as a result of our explorations revealed the first Turkish CR dataset that includes dropped pronoun annotations (of size 4K entities/22K mentions), new state-of-the-art results on Turkish CR, the first neural end-to-end Turkish CR results (70.4% F-score), the first multilingual end-to-end CR results including Turkish (yielding 1.0 percentage points improvement on Turkish) and the demonstration of the positive impact of dropped pronouns on CR of pro-dropped and morphologically rich languages, for the first time in the literature. Our research has brought Turkish end-to-end CR performances (72.0% F-score) to similar levels with other languages, surpassing the baseline scores by 32.1 percentage points. © 2024 Elsevier Ltd

Název v anglickém jazyce

Enhancing Turkish Coreference Resolution: Insights from deep learning, dropped pronouns, and multilingual transfer learning

Popis výsledku anglicky

Coreference resolution (CR), which is the identification of in-text mentions that refer to the same entity, is a crucial step in natural language understanding. While CR in English has been studied for quite a long time, studies for pro-dropped and morphologically rich languages is an active research area which has yet to reach sufficient maturity. Turkish, a morphologically highly-rich language, poses interesting challenges for natural language processing tasks, including CR, due to its agglutinative nature and consequent pronoun-dropping phenomenon. This article explores the use of different neural CR architectures (i.e., mention-pair, mention-ranking, and end-to-end) on Turkish, a morphologically highly-rich language, by formulating multiple research questions around the impacts of dropped pronouns, data quality, and interlingual transfer. The preparations made to explore these research questions and the findings obtained as a result of our explorations revealed the first Turkish CR dataset that includes dropped pronoun annotations (of size 4K entities/22K mentions), new state-of-the-art results on Turkish CR, the first neural end-to-end Turkish CR results (70.4% F-score), the first multilingual end-to-end CR results including Turkish (yielding 1.0 percentage points improvement on Turkish) and the demonstration of the positive impact of dropped pronouns on CR of pro-dropped and morphologically rich languages, for the first time in the literature. Our research has brought Turkish end-to-end CR performances (72.0% F-score) to similar levels with other languages, surpassing the baseline scores by 32.1 percentage points. © 2024 Elsevier Ltd

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2025

Kód důvěrnosti údajů

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

Název periodika

Computer Speech and Language

ISSN

0885-2308

e-ISSN

—

Svazek periodika

89

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

101681

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85196493074