gaBERT -- an Irish Language Model

TL;DR

gaBERT is a new Irish language-specific BERT model that outperforms multilingual and other monolingual models on NLP tasks, demonstrating the benefits of language-specific training and optimized tokenization.

Contribution

The paper introduces gaBERT, a monolingual Irish language BERT model, and evaluates its performance against existing models, highlighting improvements in downstream NLP tasks.

Findings

gaBERT outperforms multilingual BERT in Irish NLP tasks.

Filtering criteria and tokenization choices significantly impact performance.

Fine-tuned gaBERT surpasses mBERT in verbal multiword expression identification.

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.