Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages-Reference-Cited by-同舟云学术

Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages

Published:2023-11-10 Issue: Volume: Page:453-458
ISSN:0302-9743
Container-title:PRICAI 2023: Trends in Artificial Intelligence
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Author:

Kuwanto Garry,Akyürek Afra Feyza,Tourni Isidora Chara,Li Siyang,Jones Alex,Wijaya Derry

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-7025-4_39

Reference16 articles.

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2. Conneau, A., Lample, G.: Cross-lingual language model pretraining. In: Advances in Neural Information Processing Systems, pp. 7059–7069 (2019). https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf

3. Grover, J., Mitra, P.: Bilingual word embeddings with bucketed CNN for parallel sentence extraction. In: Proceedings of ACL 2017, Student Research Workshop, pp. 11–16. Association for Computational Linguistics, Vancouver, Canada (2017). https://www.aclweb.org/anthology/P17-3003

4. Guo, M., et al.: Effective parallel corpus mining using bilingual sentence embeddings. In: Proceedings of the Third Conference on Machine Translation: Research Papers, pp. 165–176. Association for Computational Linguistics, Brussels, Belgium (2018). https://doi.org/10.18653/v1/W18-6317, https://www.aclweb.org/anthology/W18-6317

5. Joshi, P., Santy, S., Budhiraja, A., Bali, K., Choudhury, M.: The state and fate of linguistic diversity and inclusion in the NLP world. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6282–6293. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.560, https://www.aclweb.org/anthology/2020.acl-main.560