gaBERT -- an Irish Language Model
This addresses the problem of limited NLP resources for low-resource languages like Irish, though it is incremental as it adapts an existing method to new data.
The authors tackled the lack of a high-quality monolingual BERT model for the Irish language by introducing gaBERT, which outperformed multilingual BERT and an existing Irish model in parsing and verbal multiword expression tasks.
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.