Crowdsourcing Translation Technology Based on Recurrent Neural Network Model with Bilingual Constraints

• Hui Zhao ^[1] ; Ali Bin Selama ^[1] ; Kaiyisah Hanis Binti Mohd Azm ^[1]
  1. [1] Malaysia-Japan International Institute of Technology, Universiti Technologi Malaysia, Kuala Lumpur, 54100, Malaysia
• Localización: RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação, ISSN-e 1646-9895, Nº. Extra 72, 2024, págs. 794-804
• Idioma: portugués
• Enlaces
  • Texto completo (pdf)
• Resumen

  • Neural network algorithm is a convenient and fast computer intelligent algorithm, and it is widely used in various fields of science and technology with the development of computer technology. In order to study Crowdsourcing Translation, a recurrent neural network model based on bilingual constraints was proposed, and this model was applied to the decoding process of Crowdsourcing Translation; then, a step-by-step and semi-supervised training method was proposed to train the established model; in addition, the expressions of phrases in translation are explored, and an expression based on translation confidence was proposed; finally, the performance of the whole translation was verified by bilingual constraints from Chinese to English, and the validity and correctness of the method were proved by the analysis of the experimental results.

• Referencias bibliográficas
  • Dabre R, Chu C, Kunchukuttan A. (2020). A survey of multilingual neural machine translation. ACM Computing Surveys (CSUR), 53(5), 1-38.
  • Demir S. (2022). Turkish data-to-text generation using sequence-to-sequence neural networks. ACM Transactions on Asian and Low-Resource Language...
  • Farooq, U., Mohd Rahim, M. S., & Abid, A. (2023). A multi-stack RNN-based neural machine translation model for English to Pakistan sign...
  • Goyal, N., Gao, C., Chaudhary, V., Chen, P. J., Wenzek, G., Ju, D., & Fan, A. (2022). The flores-101 evaluation benchmark for low-resource...
  • Haque, R., Liu, C. H., & Way, A. (2021). Recent advances of low-resource neural machine translation. Machine Translation, 35(4), 451-474.
  • Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions....
  • Jaworski, R., Seljan, S., & Dunđer, I. (2023). Four million segments and counting: Building an English-Croatian parallel corpus through...
  • Jiménez, C. M. A. (2021). The impact of crowdsourcing and online collaboration in professional translation: Charting the future of translation?...
  • Khan, Z., Zakira, M., Slamu, W., & Slam, N. (2020). A study of neural machine translation from Chinese to Urdu. Journal of Autonomous...
  • Maruf, S., Saleh, F., & Haffari, G. (2021). A survey on document-level neural machine translation: Methods and evaluation. ACM Computing...
  • Nath, B., Sarkar, S., Das, S., & Mukhopadhyay, S. (2024). Neural machine translation for Indian language pair using hybrid attention mechanism....
  • Raju, B. N., Raju, M. B., & Satyanarayana, K. V. V. (2021). Effective preprocessing based neural machine translation for English to Telugu...
  • Resiandi, K., Murakami, Y., & Nasution, A. H. (2023). Neural network-based bilingual lexicon induction for Indonesian ethnic languages....
  • Rivera, T. I. (2022). Machine translation systems and quality assessment: A systematic review. Language Resources and Evaluation, 56(2), 593-619.
  • Xiao, Y., Wu, L., Guo, J., Li, J., Zhang, M., Qin, T., & Liu, T. Y. (2023). A survey on non-autoregressive generation for neural machine...
  • Zakraoui, J., Saleh, M., Maadeed, S., & Alja’am, J. M. (2021). Arabic machine translation: A survey with challenges and future directions....
  • Zhang, B., Xiong, D., Xie, J., & Su, J. (2020). Neural machine translation with GRU-gated attention model. IEEE Transactions on Neural...