KU Leuven / Brepols-CTLO at EvaLatin 2024: Span extraction approaches for Latin dependency parsing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AI6AVTIU2" target="_blank" >RIV/00216208:11320/25:I6AVTIU2 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195194463&partnerID=40&md5=af2a73bf5f7901ebde97a6fc66405a5d" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195194463&partnerID=40&md5=af2a73bf5f7901ebde97a6fc66405a5d</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

KU Leuven / Brepols-CTLO at EvaLatin 2024: Span extraction approaches for Latin dependency parsing

Popis výsledku v původním jazyce

This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2024. We present the results of two runs, both of which try to implement a span extraction approach. The first run implements span-span prediction, rooted in Machine Reading Comprehension, while making use of LaBERTa, a RoBERTa model pretrained on Latin texts. The first run produces meaningful results. The second, more experimental run operates on the token-level with a span-extraction approach based on the Question Answering task. This model finetuned a DeBERTa model, pretrained on Latin texts. The finetuning was set up in the form of a Multitask Model, with classification heads for each token's part-of-speech tag and dependency relation label, while a question answering head handled the dependency head predictions. Due to the shared loss function, this paper tried to capture the link between part-of-speech tag, dependency relation and dependency heads, that follows the human intuition. The second run did not perform well. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

Název v anglickém jazyce

KU Leuven / Brepols-CTLO at EvaLatin 2024: Span extraction approaches for Latin dependency parsing

Popis výsledku anglicky

This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2024. We present the results of two runs, both of which try to implement a span extraction approach. The first run implements span-span prediction, rooted in Machine Reading Comprehension, while making use of LaBERTa, a RoBERTa model pretrained on Latin texts. The first run produces meaningful results. The second, more experimental run operates on the token-level with a span-extraction approach based on the Question Answering task. This model finetuned a DeBERTa model, pretrained on Latin texts. The finetuning was set up in the form of a Multitask Model, with classification heads for each token's part-of-speech tag and dependency relation label, while a question answering head handled the dependency head predictions. Due to the shared loss function, this paper tried to capture the link between part-of-speech tag, dependency relation and dependency heads, that follows the human intuition. The second run did not perform well. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Workshop Lang. Technol. Hist. Anc. Lang., LT4HALA LREC-COLING - Workshop Proc.

ISBN

978-249381446-3

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

203-206

Název nakladatele

European Language Resources Association (ELRA)

Místo vydání

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Místo konání akce

Torino, Italia

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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