UDParse @ SIGTYP 2024 Shared Task: Modern Language Models for Historical Languages

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AADUHP93P" target="_blank" >RIV/00216208:11320/25:ADUHP93P - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189630518&partnerID=40&md5=d11e00a79b191c385d6d7e08f310566d" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189630518&partnerID=40&md5=d11e00a79b191c385d6d7e08f310566d</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

UDParse @ SIGTYP 2024 Shared Task: Modern Language Models for Historical Languages

Popis výsledku v původním jazyce

SIGTYP’s Shared Task on Word Embedding Evaluation for Ancient and Historical Languages was proposed in two variants, constrained or unconstrained. Whereas the constrained variant disallowed any other data to train embeddings or models than the data provided, the unconstrained variant did not have these limits. We participated in the five tasks of the unconstrained variant and came out first. The tasks were the prediction of part-of-speech, lemmas and morphological features and filling masked words and masked characters on 16 historical languages. We decided to use a dependency parser and train the data using an underlying pretrained transformer model to predict part-of-speech tags, lemmas, and morphological features. For predicting masked words, we used multilingual distilBERT (with rather bad results). In order to predict masked characters, our language model is extremely small: it is a model of 5-gram frequencies, obtained by reading the available training data. © 2024 Association for Computational Linguistics.

Název v anglickém jazyce

UDParse @ SIGTYP 2024 Shared Task: Modern Language Models for Historical Languages

Popis výsledku anglicky

SIGTYP’s Shared Task on Word Embedding Evaluation for Ancient and Historical Languages was proposed in two variants, constrained or unconstrained. Whereas the constrained variant disallowed any other data to train embeddings or models than the data provided, the unconstrained variant did not have these limits. We participated in the five tasks of the unconstrained variant and came out first. The tasks were the prediction of part-of-speech, lemmas and morphological features and filling masked words and masked characters on 16 historical languages. We decided to use a dependency parser and train the data using an underlying pretrained transformer model to predict part-of-speech tags, lemmas, and morphological features. For predicting masked words, we used multilingual distilBERT (with rather bad results). In order to predict masked characters, our language model is extremely small: it is a model of 5-gram frequencies, obtained by reading the available training data. © 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

9

Strana od-do

142-150

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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