@inproceedings{partanen-etal-2019-dialect,
title = "Dialect Text Normalization to Normative Standard {F}innish",
author = {Partanen, Niko and
H{\"a}m{\"a}l{\"a}inen, Mika and
Alnajjar, Khalid},
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5519",
doi = "10.18653/v1/D19-5519",
pages = "141--146",
abstract = "We compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish. As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text. We work on a corpus consisting of dialectal data of 23 distinct Finnish dialects. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.",
}
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<abstract>We compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish. As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text. We work on a corpus consisting of dialectal data of 23 distinct Finnish dialects. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.</abstract>
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%0 Conference Proceedings
%T Dialect Text Normalization to Normative Standard Finnish
%A Partanen, Niko
%A Hämäläinen, Mika
%A Alnajjar, Khalid
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F partanen-etal-2019-dialect
%X We compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish. As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text. We work on a corpus consisting of dialectal data of 23 distinct Finnish dialects. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.
%R 10.18653/v1/D19-5519
%U https://aclanthology.org/D19-5519
%U https://doi.org/10.18653/v1/D19-5519
%P 141-146
Markdown (Informal)
[Dialect Text Normalization to Normative Standard Finnish](https://aclanthology.org/D19-5519) (Partanen et al., WNUT 2019)
ACL