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        <title>DanTok: Domain Beats Language for Danish Social Media POS Tagging</title>
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    <abstract>Language from social media remains challenging to process automatically, especially for non-English languages. In this work, we introduce the first NLP dataset for TikTok comments and the first Danish social media dataset with part-of-speech annotation. We further supply annotations for normalization, code-switching, and annotator uncertainty. As transferring models to such a highly specialized domain is non-trivial, we conduct an extensive study into which source data and modeling decisions most impact the performance. Surprisingly, transferring from in-domain data, even from a different language, outperforms in-language, out-of-domain training. These benefits nonetheless rely on the underlying language models having been at least partially pre-trained on data from the target language. Using our additional annotation layers, we further analyze how normalization, code-switching, and human uncertainty affect the tagging accuracy.</abstract>
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%0 Conference Proceedings
%T DanTok: Domain Beats Language for Danish Social Media POS Tagging
%A Kirstein Hansen, Kia
%A Barrett, Maria
%A Müller-Eberstein, Max
%A Damgaard, Cathrine
%A Eriksen, Trine
%A Goot, Rob
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F kirstein-hansen-etal-2023-dantok
%X Language from social media remains challenging to process automatically, especially for non-English languages. In this work, we introduce the first NLP dataset for TikTok comments and the first Danish social media dataset with part-of-speech annotation. We further supply annotations for normalization, code-switching, and annotator uncertainty. As transferring models to such a highly specialized domain is non-trivial, we conduct an extensive study into which source data and modeling decisions most impact the performance. Surprisingly, transferring from in-domain data, even from a different language, outperforms in-language, out-of-domain training. These benefits nonetheless rely on the underlying language models having been at least partially pre-trained on data from the target language. Using our additional annotation layers, we further analyze how normalization, code-switching, and human uncertainty affect the tagging accuracy.
%U https://aclanthology.org/2023.nodalida-1.27
%P 271-279

Markdown (Informal)

[DanTok: Domain Beats Language for Danish Social Media POS Tagging](https://aclanthology.org/2023.nodalida-1.27) (Kirstein Hansen et al., NoDaLiDa 2023)

• DanTok: Domain Beats Language for Danish Social Media POS Tagging (Kirstein Hansen et al., NoDaLiDa 2023)

ACL

• Kia Kirstein Hansen, Maria Barrett, Max Müller-Eberstein, Cathrine Damgaard, Trine Eriksen, and Rob Goot. 2023. DanTok: Domain Beats Language for Danish Social Media POS Tagging. In Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 271–279, Tórshavn, Faroe Islands. University of Tartu Library.