ga BERT — an I rish Language Model (2022)

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.

## md5 7be5c0e9bc473fb83af13541b1cd8d20 for full-stop only: additional candidate split points are added with the following heuristics. furthermore, when we need to choose between multiple candidate split points that pass the above tests, we try to keep the lengths of the halves (in characters) similar but also factor in the preferences in the heuristics below.