gaBERT -- an Irish Language Model

2021/07/27 by James Barry, Barry, James, Joachim Wagner +13 · 2 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #cs.CL

paper · pdf · doi:10.48550/arxiv.2107.12930

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)

arxiv created 2022/06/28 · arxiv updated 2022/06/29

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.

Cited by

• Irish-BLiMP: A Linguistic Benchmark for Evaluating Human and Language Model Performance in a Low-Resource Setting
• Qomhra: A Bilingual Irish and English Large Language Model

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