BERTrade: Using Contextual Embeddings to Parse Old French

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AZJWT49Z5" target="_blank" >RIV/00216208:11320/22:ZJWT49Z5 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2022.lrec-1.119" target="_blank" >https://aclanthology.org/2022.lrec-1.119</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

BERTrade: Using Contextual Embeddings to Parse Old French

Popis výsledku v původním jazyce

The successes of contextual word embeddings learned by training large-scale language models, while remarkable, have mostly occurred for languages where significant amounts of raw texts are available and where annotated data in downstream tasks have a relatively regular spelling. Conversely, it is not yet completely clear if these models are also well suited for lesser-resourced and more irregular languages. We study the case of Old French, which is in the interesting position of having relatively limited amount of available raw text, but enough annotated resources to assess the relevance of contextual word embedding models for downstream NLP tasks. In particular, we use POS-tagging and dependency parsing to evaluate the quality of such models in a large array of configurations, including models trained from scratch from small amounts of raw text and models pre-trained on other languages but fine-tuned on Medieval French data.

Název v anglickém jazyce

BERTrade: Using Contextual Embeddings to Parse Old French

Popis výsledku anglicky

The successes of contextual word embeddings learned by training large-scale language models, while remarkable, have mostly occurred for languages where significant amounts of raw texts are available and where annotated data in downstream tasks have a relatively regular spelling. Conversely, it is not yet completely clear if these models are also well suited for lesser-resourced and more irregular languages. We study the case of Old French, which is in the interesting position of having relatively limited amount of available raw text, but enough annotated resources to assess the relevance of contextual word embedding models for downstream NLP tasks. In particular, we use POS-tagging and dependency parsing to evaluate the quality of such models in a large array of configurations, including models trained from scratch from small amounts of raw text and models pre-trained on other languages but fine-tuned on Medieval French data.

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Thirteenth Language Resources and Evaluation Conference

ISBN

979-10-95546-72-6

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

1104-1113

Název nakladatele

European Language Resources Association

Místo vydání

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Místo konání akce

Marseille, France

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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