Morphological Segmentation with Neural Networks: Performance Effects of Architecture, Data Size, and Cross-Lingual Transfer in Seven Languages

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511638" target="_blank" >RIV/00216208:11320/25:10511638 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Morphological Segmentation with Neural Networks: Performance Effects of Architecture, Data Size, and Cross-Lingual Transfer in Seven Languages

Popis výsledku v původním jazyce

We present a comparison of neural network-based morphological segmenters trained on morphologically segmented datasets from seven European languages: Czech, English, French, German, Italian, Dutch, and Slovak. Our aim is to investigate how different model architectures and dataset sizes influence segmentation quality, and how performance varies across languages. To this end, we evaluate recurrent and convolutional neural network models and compare them to widely used unsupervised baseline methods. In selecting the datasets, we prioritized linguistic accuracy and segmentation completeness. We also explore the impact of cross-lingual transfer learning on model performance. Our results show that neural models trained on as few as 125 words outperform unsupervised methods. Moreover, for closely related languages, zero-shot cross-lingual transfer learning can also surpass unsupervised baselines. Overall, we observe consistent performance patterns across languages.

Název v anglickém jazyce

Morphological Segmentation with Neural Networks: Performance Effects of Architecture, Data Size, and Cross-Lingual Transfer in Seven Languages

Popis výsledku anglicky

We present a comparison of neural network-based morphological segmenters trained on morphologically segmented datasets from seven European languages: Czech, English, French, German, Italian, Dutch, and Slovak. Our aim is to investigate how different model architectures and dataset sizes influence segmentation quality, and how performance varies across languages. To this end, we evaluate recurrent and convolutional neural network models and compare them to widely used unsupervised baseline methods. In selecting the datasets, we prioritized linguistic accuracy and segmentation completeness. We also explore the impact of cross-lingual transfer learning on model performance. Our results show that neural models trained on as few as 125 words outperform unsupervised methods. Moreover, for closely related languages, zero-shot cross-lingual transfer learning can also surpass unsupervised baselines. Overall, we observe consistent performance patterns across languages.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

28th International Conference on Text, Speech and Dialogue (Part II)

ISBN

978-3-032-02551-7

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

275-286

Název nakladatele

Springer

Místo vydání

Cham, Switzerland

Místo konání akce

Erlangen, Germany

Datum konání akce

25. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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