Abstract
Neural networks are widely used for the task of diacritics restoration last years. Authors use different architectures of neural network for selected languages. In this paper, we demonstrated that an architecture should be selected according to a language in hand. It also depends on a task one states: low and full resourced languages could use different architectures. We demonstrated that common used accuracy metric should be changed in this task to precision and recall due to the heavy unbalanced nature of the input data. The paper contains results for seven languages: Croatian, Slovak, Romanian, French, German, Latvian, and Turkish.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We extracted 5-grams in our preliminary experiments, but achieved remarkably worse results not published here.
- 2.
Links to used data and source codes are placed at GitHub: https://github.com/klyshinsky/diacritics_restoration.
- 3.
We want to thank Google Colab for the provided GPU time. It was not enough for all our calculations, but it was very useful.
References
Yarowsky, D.: Comparison of corpus-based techniques for restoring accents in Spanish and French text. Natural Language Processing using Very Large Corpora 1999, pp. 99–120. Springer, Dordrecht (1999)
Tufiş, D., Chiţu, A.: Automatic diacritics insertion in Romanian texts. In: Proceedings of COMPLEX 1999 International Conference on Computational Lexicography, Pecs, Hungary, pp. 185–194 (1999)
Ezeani, I., Hepple, M., Onyenwe, I.: Automatic restoration of diacritics for Igbo language. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2016. LNCS (LNAI), vol. 9924, pp. 198–205. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45510-5_23
Schlippe, T., Nguyen, T.L., Vogel, S. E.: Diacritization as a machine translation problem and as a sequence labeling problem. In: Proceedings of the Eighth Conference of the Association for Machine Translation in the Americas, pp. 270–278 (2008)
Náplava, J., Straka, M., Straňák, P., Hajič, J.: Diacritics restoration using neural networks. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 1566–1573 (2018)
Alqahtani, S., Mishra, A., Diab, M.: Efficient convolutional neural networks for diacritic restoration. In: Proceedings of the 9th International Joint Conference on Natural Language Processing, pp. 1442–1448 (2019)
Orife, I.: attentive sequence-to-sequence learning for diacritic restoration of Yorùbá language text. In: Proceedings of the INTERSPEECH 2018, pp. 2848–2852 (2018). https://doi.org/10.21437/Interspeech.2018-42
De Pauw, G., Wagacha, P.W., de Schryver, G.-M.: Automatic diacritic restoration for resource-scarce languages. In: Proceedings of the Text, Speech and Dialogue, 10th International Conference, pp. 170–179 (2007). https://doi.org/10.1007/978-3-540-74628-7_24
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Klyshinsky, E., Karpik, O., Bondarenko, A. (2021). A Comparison of Neural Networks Architectures for Diacritics Restoration. In: van der Aalst, W.M.P., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2020. Communications in Computer and Information Science, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-71214-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-71214-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71213-6
Online ISBN: 978-3-030-71214-3
eBook Packages: Computer ScienceComputer Science (R0)