Abstract
In this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types of monolingual ELECTRA and ConvBERT models and compare our results to a previously trained monolingual RoBERTa model and the multilingual mBERT model. We find that the Transformer models obtain better results, often by a large margin, compared to previous state-of-the-art models. Furthermore, our results indicate that pre-training larger language models results in a significant reduction in error rates in comparison to smaller models. Finally, our results show that the monolingual models for Icelandic outperform a comparably sized multilingual model.- Anthology ID:
- 2022.lrec-1.804
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 7386–7391
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.804
- DOI:
- Cite (ACL):
- Jón Friðrik Daðason and Hrafn Loftsson. 2022. Pre-training and Evaluating Transformer-based Language Models for Icelandic. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 7386–7391, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Pre-training and Evaluating Transformer-based Language Models for Icelandic (Daðason & Loftsson, LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2022.lrec-1.804.pdf