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- W4386566966 abstract "This paper analyzes a Named Entity Recognition task for South-Slavic languages using the pre-trained multilingual neural network models. We investigate whether the performance of the models for a target language can be improved by using data from closely related languages. We have shown that the model performance is not influenced substantially when trained with other than a target language. While for Slovene, the monolingual setting generally performs better, for Croatian and Serbian the results are slightly better in selected cross-lingual settings, but the improvements are not large. The most significant performance improvement is shown for the Serbian language, which has the smallest corpora. Therefore, fine-tuning with other closely related languages may benefit only the “low resource” languages." @default.
- W4386566966 created "2023-09-10" @default.
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- W4386566966 date "2023-01-01" @default.
- W4386566966 modified "2023-09-28" @default.
- W4386566966 title "Analysis of Transfer Learning for Named Entity Recognition in South-Slavic Languages" @default.
- W4386566966 doi "https://doi.org/10.18653/v1/2023.bsnlp-1.13" @default.
- W4386566966 hasPublicationYear "2023" @default.
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