Char-RNN for Word Stress Detection in East Slavic Languages
Computation and Language
2023-10-04 v1
Abstract
We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russian, Ukranian, Belarusian languages. We present new datasets, annotated with the word stress, for the three languages and compare several RNN models trained on three languages and explore possible applications of the transfer learning for the task. We show that it is possible to train a model in a cross-lingual setting and that using additional languages improves the quality of the results.
Keywords
Cite
@article{arxiv.1906.04082,
title = {Char-RNN for Word Stress Detection in East Slavic Languages},
author = {Ekaterina Chernyak and Maria Ponomareva and Kirill Milintsevich},
journal= {arXiv preprint arXiv:1906.04082},
year = {2023}
}
Comments
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects at NAACL-2019