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Africa 
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Futurity of Translation Algorithms for Neural Machine Translation (NMT) and Its Vision
Published in Brojo Kishore Mishra, Raghvendra Kumar, Natural Language Processing in Artificial Intelligence, 2020
K. Mandal, G. S. Pradeep Ghantasala, Firoz Khan, R. Sathiyaraj, B. Balamurugan
The post-editing method provides better translation accuracy but considers the post-editing time requirement. Post-editing effort was to perform the minimum amount of substation, unwanted word deletions, or necessary translated word insertions made in few words or in the entire sentence of machine translation gains the output in the exact meaning of target language with source language [75]. Still, the translation of NMT working in a finer way compare to the traditional process, but sometimes the preliminary results might not be satisfied as come to SMT. Recent trends of comparing the post-editing SMT with NMT predicted output contains the same data for both approaches, the measurement was based on editing time and post-editing effort for technical aspects based on a number of keystrokes utilized on the editing process. Not only with that, even has it quantified the number of keystrokes nonutilized words on translation. This projects the advantages of NMT over others on post-editing efforts based on keystrokes [76].
Improving Efficiency and Accuracy in English Translation Learning: Investigating a Semantic Analysis Correction Algorithm
Published in Applied Artificial Intelligence, 2023
To overcome limitations that are commonly encountered in translation systems, here are a few strategies that can be employed: Training Data Expansion: Increasing the size and variety of the training sample can help improve the performance of translation systems. By incorporating a larger and more varied dataset, the system can learn from a wider range of translation examples, leading to better generalization and accuracy. This can involve gathering additional parallel corpora or utilizing techniques like data augmentation and domain adaptation to enhance the training data.Fine-tuning and Model Optimization: Fine-tuning the system and optimizing the model parameters based on specific evaluation metrics can help address limitations. Techniques like regularization, hyperparameter tuning, and ensemble methods can be employed to improve the model’s performance and robustness. Iterative refinement through experimentation and evaluation can lead to more effective translations.Incorporating Context and Discourse: Consideration of the broader context and discourse can enhance translation quality. Language is inherently context-dependent, and incorporating information from preceding and succeeding sentences can help resolve ambiguities and improve translation accuracy. Techniques such as neural machine translation with attention mechanisms or transformer models can capture contextual information effectively.Post-editing and Human-in-the-Loop Approaches: Incorporating human expertise through post-editing can help refine and improve the translations produced by the system. Combining automated translation with human input allows for the correction of errors and the addition of domain-specific knowledge or nuances. Human-in-the-loop approaches, such as interactive translation interfaces or crowdsourcing, can be leveraged to iteratively enhance the translation output.Continuous Evaluation and User Feedback: Establishing a feedback loop for continuous evaluation and improvement is crucial. Collecting user feedback, analyzing translation quality, and addressing user preferences and specific needs can guide system enhancements. Monitoring and adapting the system based on ongoing evaluation and user input can lead to iterative improvements.