Behr at EvaLatin 2024: Latin Dependency Parsing Using Historical Sentence Embeddings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ADLBSJT6M" target="_blank" >RIV/00216208:11320/25:DLBSJT6M - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195176114&partnerID=40&md5=69c3c78bc426f034eba61d968b40f787" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195176114&partnerID=40&md5=69c3c78bc426f034eba61d968b40f787</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Behr at EvaLatin 2024: Latin Dependency Parsing Using Historical Sentence Embeddings

Popis výsledku v původním jazyce

This paper identifies the system used for my submission to EvaLatin’s shared dependency parsing task as part of the LT4HALA 2024 workshop. EvaLatin presented new Latin prose and poetry dependency test data from potentially different time periods, and imposed no restriction on training data or model selection for the task. This paper, therefore, sought to build a general Latin dependency parser that would perform accurately regardless of the Latin age to which the test data belongs. To train a general parser, all of the available Universal Dependencies treebanks were used, but in order to address the changes in the Latin language over time, this paper introduces historical sentence embeddings. A model was trained to encode sentences of the same Latin age into vectors of high cosine similarity, which are referred to as historical sentence embeddings. The system introduces these historical sentence embeddings into a biaffine dependency parser with the hopes of enabling training across the Latin treebanks in a more efficacious manner, but their inclusion shows no improvement over the base model. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

Název v anglickém jazyce

Behr at EvaLatin 2024: Latin Dependency Parsing Using Historical Sentence Embeddings

Popis výsledku anglicky

This paper identifies the system used for my submission to EvaLatin’s shared dependency parsing task as part of the LT4HALA 2024 workshop. EvaLatin presented new Latin prose and poetry dependency test data from potentially different time periods, and imposed no restriction on training data or model selection for the task. This paper, therefore, sought to build a general Latin dependency parser that would perform accurately regardless of the Latin age to which the test data belongs. To train a general parser, all of the available Universal Dependencies treebanks were used, but in order to address the changes in the Latin language over time, this paper introduces historical sentence embeddings. A model was trained to encode sentences of the same Latin age into vectors of high cosine similarity, which are referred to as historical sentence embeddings. The system introduces these historical sentence embeddings into a biaffine dependency parser with the hopes of enabling training across the Latin treebanks in a more efficacious manner, but their inclusion shows no improvement over the base model. © 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

5

Strana od-do

198-202

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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