gaBERT -- an Irish Language Model
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.
Cite
@article{arxiv.2107.12930,
title = {gaBERT -- an Irish Language Model},
author = {James Barry and Joachim Wagner and Lauren Cassidy and Alan Cowap and Teresa Lynn and Abigail Walsh and Mícheál J. Ó Meachair and Jennifer Foster},
journal= {arXiv preprint arXiv:2107.12930},
year = {2022}
}
Comments
Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 4774-4788, Marseille, France, 20-25 June 2022, European Language Resources Association (ELRA)