Benchmarking pre-trained language models for multilingual NER: TraSpaS at the BSNLP2021 shared task
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441730" target="_blank" >RIV/00216208:11320/21:10441730 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Benchmarking pre-trained language models for multilingual NER: TraSpaS at the BSNLP2021 shared task
Original language description
In this paper we describe TraSpaS, a submission to the third shared task on named entity recognition hosted as part of the Balto-Slavic Natural Language Processing (BSNLP) Workshop. In it we evaluate various pre-trained language models on the NER task using three open-source NLP toolkits: character level language model with Stanza, language-specific BERT-style models with SpaCy and Adapter-enabled XLM-R with Trankit. Our results show that the Trankit-based models outperformed those based on the other two toolkits, even when trained on smaller amounts of data. Our code is available at https://github.com/NaiveNeuron/slavner-2021.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2021
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 8th BSNLP Workshop on Balto-Slavic Natural Language Processing, BSNLP 2021 - Co-located with the 16th European Chapter of the Association for Computational Linguistics, EACL 2021
ISBN
978-1-954085-14-5
ISSN
—
e-ISSN
—
Number of pages
10
Pages from-to
105-114
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg
Event location
Kyjev
Event date
Apr 20, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—