Haukur Barri Símonarson


A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models
Vésteinn Snæbjarnarson | Haukur Barri Símonarson | Pétur Orri Ragnarsson | Svanhvít Lilja Ingólfsdóttir | Haukur Jónsson | Vilhjalmur Thorsteinsson | Hafsteinn Einarsson
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks, including part-of-speech tagging, named entity recognition, grammatical error detection and constituency parsing. To train the models we introduce a new corpus of Icelandic text, the Icelandic Common Crawl Corpus (IC3), a collection of high quality texts found online by targeting the Icelandic top-level-domain .is. Several other public data sources are also collected for a total of 16GB of Icelandic text. To enhance the evaluation of model performance and to raise the bar in baselines for Icelandic, we manually translate and adapt the WinoGrande commonsense reasoning dataset. Through these efforts we demonstrate that a properly cleaned crawled corpus is sufficient to achieve state-of-the-art results in NLP applications for low to medium resource languages, by comparison with models trained on a curated corpus. We further show that initializing models using existing multilingual models can lead to state-of-the-art results for some downstream tasks.


Miðeind’s WMT 2021 Submission
Haukur Barri Símonarson | Vésteinn Snæbjarnarson | Pétur Orri Ragnarson | Haukur Jónsson | Vilhjalmur Thorsteinsson
Proceedings of the Sixth Conference on Machine Translation

We present Miðeind’s submission for the English→Icelandic and Icelandic→English subsets of the 2021 WMT news translation task. Transformer-base models are trained for translation on parallel data to generate backtranslations teratively. A pretrained mBART-25 model is then adapted for translation using parallel data as well as the last backtranslation iteration. This adapted pretrained model is then used to re-generate backtranslations, and the training of the adapted model is continued.