Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation
Keywords:English translation, corpus analysis, semantic analysis, correction algorithm
INTRODUCTION: Machine translation is a modern natural language processing research field with important scientific and practical significance. In practice, the variation of languages, the limitation of semantic knowledge, and the lack of parallel language resources limit the development of machine translation.
OBJECTIVES: This paper aims to avoid duplicating neural networks during the learning process and improve the ability to generalize complex neural network machine translation models with limited resources.
METHODS: Textual material in the source language was studied, and a suitable textual material representation model was used to express complex, high-level, and abstract semantic information. Then, a more efficient neural network machine translation integration model was developed based on the control of written data and algorithms.
RESULTS: Data mining must be applied to complex neural network machine translation systems based on transfer learning to standardize finite neural network models.
CONCLUSION: Neural network-based embedded machine translation systems based on migration training require a small number of labelled samples to improve the system's permeability. However, this adaptive migration learning region approach can easily lead to over-learning problems in neural network machine translation models, thus avoiding excessive correspondences during the learning process and improving the generalization ability of the translation model with limited neural network resources.
Shafei B. The Treatment of Some Deviation Cases in Non-Standard Data Using Artificial Intelligence and Natural Language Processing Applied to Arabic Language. 6. 2022;5(4):12–9.
Sauerland MBU, Sauerland U, Uli Sauerland Uli SauerlandLeibniz-Centre General Linguistics ZAS G by US Schützenstrasse, Berlin, and, Gould MBKA, Gould KA, et al. Risks and Benefits of Large Language Models for the Environment. Environmental Science And Technology. 2023;57(9):3464–6.
Liu Y, Rui X, Li Y. Long-term development archive of the Yellow River since the Neogene in the central Jinshaan Gorge, China. Palaeogeography, Palaeoclimatology, Palaeoecology: An International Journal for the Geo-Sciences. 2022;5(591-):591.
Allen HW, Paesani K. Genre instruction, textual borrowing, and foreign language writing: Graduate teaching assistant perspectives and practices: Language Teaching Research. 2022;26(4):755–76.
Liu C, Wang K, Wu A. Management and Monitoring of Multi-Behavior Recommendation Systems Using Graph Convolutional Neural Networks. International Journal of Foundations of Computer Science. 2023;8(27):120-124..
Zhang J, Wang F. A Better Medical Interpreting Service: Interpreter’s Roles and Strategies Under Goffman’s Participation Framework. International Journal of Translation Interpretation and Applied Linguistics. 2021;3(1):1–14.
Alhassan A, Sabtan YMN, Omar L. Using Parallel Corpora in the Translation Classroom: Moving towards a Corpus-driven Pedagogy for Omani Translation Major Students. Arab World English Journal. 2021;12(1):40–58.
Wang J, Bai L. Unveiling the Scoring Validity of Two Chinese Automated Writing Evaluation Systems: A Quantitative Study. International Journal of English Linguistics. 2021;2(2):56–60.
Qiu G, Su F, Gao L, Chen CC. Research on Translation Style in Machine Learning Based on Linguistic Quantitative Characteristics Perception. Sensors and materials: An International Journal on Sensor Technology. 2021;5(6 Pt.2):33.
Bundgaard-Nielsen RL, O’Shannessy C. When more is more: The mixed language Light Warlpiri amalgamates source language phonologies to form a near-maximal inventory. Journal of Phonetics. 2021;85(4):101037.
Xiang H Aijun Su. Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with an attention mechanism. Measurement. 2021;175(1):34–40.
Novitskiy N, Maggu AR, Lai CM, Chan PHY, Wong P. Early Development of Neural Speech Encoding Depends on Age but not Native Language Status: Evidence from Lexical Tone. Neurobiology of Language. 2021;12(8):1–59.
Gulcu S. Training of the feed-forward artificial neural networks using dragonfly algorithm. Applied Soft Computing. 2022;12(12):124.
Zhang H, Liu Y, Yang L, Hou G, Shao B, Xia J, et al. Numerical modelling of porphyroblasts rotation in the brittle-viscous transition zone. Journal of Structural Geology. 2021;24(148-Jul.):1–13.
Fantinuoli C, Prandi B. Towards evaluating simultaneous speech translation from a communicative perspective. 12. 2021;18(10):23–30.
Fu Y, Nederhof MJ. Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity. 10. 2021;6(6):16–20.
Kan W, Dechao L. Are translated Chinese Wuxia fiction and Western heroic literature similar? A stylometric analysis based on stylistic panoramas. Digital Scholarship in the Humanities. 2022;3(4):4.
Fabunan AG. Machine Translation’s Role: in Literary Translations. Multilingual. 2023;12(1):34–46.
Wang Y, Daghigh AJ. Cognitive effort in human and machine translation post-editing processes: A holistic and phased view. FORUM. 2023;21(1):139–62.
Zhang Y, Li L, Wu Y, Su Q, Sun X. Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge. IEEE/ACM transactions on audio, speech, and language processing. 2022;20(30-):30.
How to Cite
Copyright (c) 2023 Lei Shi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.