Generative Byte-Level Models for Restoring Spaces, Punctuation, and Capitalization in Multiple Languages

Author:

Dyer Laurence,Hughes Anthony,Can Burcu

Publisher

Springer International Publishing

Reference39 articles.

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2. Bakare, A.M., Anbananthen, K.S.M., Muthaiyah, S., Krishnan, J., Kannan, S.: Punctuation restoration with transformer model on social media data. Appl. Sci. 13(3), 1685 (2023). https://doi.org/10.3390/app13031685. https://www.mdpi.com/2076-3417/13/3/1685

3. Chay-intr, T., Kamigaito, H., Okumura, M.: Character-based Thai word segmentation with multiple attentions. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pp. 264–273. INCOMA Ltd., Held Online (2021). https://aclanthology.org/2021.ranlp-1.31

4. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., Stoyanov, V.: Unsupervised cross-lingual representation learning at scale. Computing Research Repository arXiv:911.02116 (2019). http://arxiv.org/abs/1911.02116. Version 2

5. Courtland, M., Faulkner, A., McElvain, G.: Efficient automatic punctuation restoration using bidirectional transformers with robust inference. In: Proceedings of the 17th International Conference on Spoken Language Translation, pp. 272–279. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.iwslt-1.33. https://aclanthology.org/2020.iwslt-1.33

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