Skip to main content

Neural Machine Translation for Native Language Aymara to English

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 561))

Included in the following conference series:

Abstract

In Latin America, there is a culture called Aymara, which has its own language also named Aymara. It is a native language in danger of extension declared by UNESCO and a heritage of the Peruvian nation. The work of Neural Machine Translator since its appearance has been able to translate many languages of the world, however it is not very well researched with native languages, in this work we experience for the first time the automatic translation from Aymara to English with the seq2seq model. First interesting results were obtained that could open up new research projects.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ministerio de la cultura del Peru, Base de datos de pueblos indiginas u originarios (2022)

    Google Scholar 

  2. Albó, X., et al.: Raices de América: el mundo aymara, 1a ed., Alianza Editorial (1988). ISBN 84-206-4213-4

    Google Scholar 

  3. Zhou, M., Secha, J., Cai, R.: Domain adaptation for Tibetan-Chinese neural machine translation. In: 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence (ACAI 2020). Association for Computing Machinery, New York, NY, USA, Article 77, 1–5 (2020). https://doi.org/10.1145/3446132.3446404

  4. Tse, R., Mirri, S., Tang, S.-K., Pau, G., Salomoni, P.: Building an Italian-Chinese parallel corpus for machine translation from the web. In: Proceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good (GoodTechs 2020). Association for Computing Machinery, New York, NY, USA, pp. 265–268 (2020). https://doi.org/10.1145/3411170.3411258

  5. Duan, C., et al.: Modeling future cost for neural machine translation. IEEE/ACM Trans. Audio, Speech and Lang. Proc. 29 (2021), 770-781 (2021). https://doi.org/10.1109/TASLP.2020.3042006

  6. Aruskipawinaka, A.: Conversaciones en aimara, Román Pairumani Ajacopa and Alejandra Bertha Carrasco Lima, Centro de Apoyo en Investigación y Educación Multidisciplinaria - CAIEM (2022)

    Google Scholar 

  7. Zanini, N., Dhawan, V.: Text Mining: An introduction to theory and some applications. Research Matters, pp. 38–44 (2015)

    Google Scholar 

  8. Huayhua Pari, F.: Normas para el buen uso de la ortografía aimara. Lengua Y Sociedad, 12(1), 167–176 (2017). Recuperado a partir de http://revista.letras.unmsm.edu.pe/index.php/ls/article/view/428

  9. Webster, J.J., Kit, C.: Tokenization as the initial phase in NLP. In: Proceedings of the 14th Conference on Computational linguistics - Volume 4, COLING ’92. Association for Computational Linguistics, USA, pp. 1106–1110 (1992). https://doi.org/10.3115/992424.992434

  10. Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation (2015). https://doi.org/10.48550/arxiv.1508.04025

  11. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org

    Google Scholar 

  12. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate (2014). https://doi.org/10.48550/arxiv.1409.0473

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Honorio Apaza .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Apaza, H., Aruhuanca, B., Nina, M.M., Flores, A., Silva, C., Tito, E. (2023). Neural Machine Translation for Native Language Aymara to English. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-18344-7_40

Download citation

Publish with us

Policies and ethics