BERTrade: Using Contextual Embeddings to Parse Old French
The result's identifiers
Result code in 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>
Result on the web
<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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Alternative languages
Result language
angličtina
Original language name
BERTrade: Using Contextual Embeddings to Parse Old French
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
10
Pages from-to
1104-1113
Publisher name
European Language Resources Association
Place of publication
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Event location
Marseille, France
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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