Findings of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Findings of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages

Popis výsledku v původním jazyce

This paper discusses the organisation and findings of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages. The shared task was split into the constrained and unconstrained tracks and involved solving either three or five problems for 12+ ancient and historical languages belonging to four language families and making use of six different scripts. There were 14 registrations in total, of which three teams participated in each track. Out of these six submissions, two systems were successful in the constrained setting and another two in the unconstrained setting, and four system description papers were submitted by different teams. The best average results for POS-tagging, lemmatisation and morphological feature prediction were 96.09%, 94.88% and 96.68% respectively. In the mask filling problem, the winning team could not achieve a higher average score across all 16 languages than 5.95% at the word level, which demonstrates the difficulty of this problem. At the character level, the best average result over 16 languages was 55.62%. © 2024 Association for Computational Linguistics.

Název v anglickém jazyce

Findings of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages

Popis výsledku anglicky

This paper discusses the organisation and findings of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages. The shared task was split into the constrained and unconstrained tracks and involved solving either three or five problems for 12+ ancient and historical languages belonging to four language families and making use of six different scripts. There were 14 registrations in total, of which three teams participated in each track. Out of these six submissions, two systems were successful in the constrained setting and another two in the unconstrained setting, and four system description papers were submitted by different teams. The best average results for POS-tagging, lemmatisation and morphological feature prediction were 96.09%, 94.88% and 96.68% respectively. In the mask filling problem, the winning team could not achieve a higher average score across all 16 languages than 5.95% at the word level, which demonstrates the difficulty of this problem. At the character level, the best average result over 16 languages was 55.62%. © 2024 Association for Computational Linguistics.

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

SIGTYP - Workshop Res. Comput. Linguist. Typology Multiling. NLP, Proc. Workshop

ISBN

979-889176071-4

ISSN

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e-ISSN

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Počet stran výsledku

13

Strana od-do

160-172

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

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Místo konání akce

St. Julian's, Malta

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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