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.- Anthology ID:
- W19-1404
- Volume:
- Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
- Month:
- June
- Year:
- 2019
- Address:
- Ann Arbor, Michigan
- Editors:
- Marcos Zampieri, Preslav Nakov, Shervin Malmasi, Nikola Ljubešić, Jörg Tiedemann, Ahmed Ali
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 35–41
- Language:
- URL:
- https://aclanthology.org/W19-1404
- DOI:
- 10.18653/v1/W19-1404
- Cite (ACL):
- Ekaterina Chernyak, Maria Ponomareva, and Kirill Milintsevich. 2019. Char-RNN for Word Stress Detection in East Slavic Languages. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 35–41, Ann Arbor, Michigan. Association for Computational Linguistics.
- Cite (Informal):
- Char-RNN for Word Stress Detection in East Slavic Languages (Chernyak et al., VarDial 2019)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/W19-1404.pdf