gaBERT — an Irish Language Model

James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, Jennifer Foster

Abstract
The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.
Anthology ID:
2022.lrec-1.511
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4774–4788
Language:
URL:
https://aclanthology.org/2022.lrec-1.511
DOI:
Bibkey:
Cite (ACL):
James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, and Jennifer Foster. 2022. gaBERT — an Irish Language Model. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4774–4788, Marseille, France. European Language Resources Association.
Cite (Informal):
gaBERT — an Irish Language Model (Barry et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/add_acl24_videos/2022.lrec-1.511.pdf
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