A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models
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A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models. / Snæbjarnarson, Vésteinn; Símonarson, Haukur Barri; Ragnarsson, Pétur Orri; Ingólfsdóttir, Svanhvít Lilja; Jónsson, Haukur Páll; Porsteinsson, Vilhjálmur; Einarsson, Hafsteinn.
2022 Language Resources and Evaluation Conference, LREC 2022. ed. / Nicoletta Calzolari; Frederic Bechet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Helene Mazo; Jan Odijk; Stelios Piperidis. European Language Resources Association (ELRA), 2022. p. 4356-4366.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models
AU - Snæbjarnarson, Vésteinn
AU - Símonarson, Haukur Barri
AU - Ragnarsson, Pétur Orri
AU - Ingólfsdóttir, Svanhvít Lilja
AU - Jónsson, Haukur Páll
AU - Porsteinsson, Vilhjálmur
AU - Einarsson, Hafsteinn
N1 - Funding Information: We thank Prof. Dr.-Ing. Morris Riedel and his team for providing access to the DEEP super-computer at Forschungszentrum Jülich. We also thank the Icelandic Language Technology Program (Nikulásdóttir et al., 2020). It has enabled the authors to focus on work in Icelandic NLP. Finally, we thank the anonymous reviewers for their helpful feedback. Publisher Copyright: © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - co-reference resolution
KW - corpus
KW - IceBERT
KW - Icelandic
KW - language model
KW - named entity recognition
KW - natural language understanding
KW - parsing
KW - part of speech
UR - http://www.scopus.com/inward/record.url?scp=85137484516&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:85137484516
SP - 4356
EP - 4366
BT - 2022 Language Resources and Evaluation Conference, LREC 2022
A2 - Calzolari, Nicoletta
A2 - Bechet, Frederic
A2 - Blache, Philippe
A2 - Choukri, Khalid
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Mariani, Joseph
A2 - Mazo, Helene
A2 - Odijk, Jan
A2 - Piperidis, Stelios
PB - European Language Resources Association (ELRA)
T2 - 13th International Conference on Language Resources and Evaluation Conference, LREC 2022
Y2 - 20 June 2022 through 25 June 2022
ER -
ID: 371184890