A Scenario-Generic Neural Machine Translation Data Augmentation Method
Open accessAbstract
Amid the rapid advancement of neural machine translation, the challenge of data sparsity has been a major obstacle. To address this issue, this study proposes a general data augmentation technique for various scenarios. It examines the predicament of parallel corpora diversity and high quality in both rich- and low-resource settings, and integrates the low-frequency word substitution method and reverse translation approach for complementary benefits. Additionally, this method improves the pseudo-parallel corpus generated by the reverse translation method by substituting low-frequency words and includes a grammar error correction module to reduce grammatical errors in low-resource scenarios. The experimental data are partitioned into rich- and low-resource scenarios at a 10:1 ratio. It verifies the necessity of grammatical error correction for pseudo-corpus in low-resource scenarios. Models and methods are chosen from the backbone network and related literature for comparative experiments. The experimental findings demonstrate that the data augmentation approach proposed in this study is suitable for both rich- and low-resource scenarios and is effective in enhancing the training corpus to improve the performance of translation tasks.
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The quality and accuracy of the explanations produced by GPT may depend on the quality and accuracy of the training data, as well as the complexity and character of the black-box model being explained.
“Notably, the quality and accuracy of the explanations produced by GPT may depend on the quality and accuracy of the training data, as well as the complexity and character of the black-box model being explained [188,189].”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [188]
The Discussion and Experimental Results discuss how corpus quality affects neural machine translation, not GPT-generated explanations. Searches for GPT explanations, black-box models, and the claimed dependence on training-data quality found no passage addressing that claim. No page numbers were available in our copy. (LLM, conf=0.76)
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Computer-Aided English Translation system based on Text-to-Text Transfer Transformer model
Cheng, Y. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934412 · Cited by 1 · see citing works
“AXiner Liu et al. [17] presented an improved method of general data augmentation adaptable to different scenarios, fusing low-frequency word substitution with back-translation.”
Li, H. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934603 · Cited by 1 · see citing works
“The Machine Translation (MT) methods are widely used for rules, examples and statistics and the methods uses vocabulary rules, syntax generation, rules for deriving analysis text processing for dictionary [3].”
Deep Learning Techniques for Machine Translation: A Survey
Hameed, D. A., & Al‐Khateeb, B. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.339 · Cited by 1 · see citing works
A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution
Tsoulos, I. G., & Tzallas, A. T. (2023) · Applied Sciences · doi:10.3390/app13148124
Zeng, Z., He, J., Xiang, T., Wang, N., Chen, B., & Guo, S. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10315-y
“• Backtranslation [52]: It is another widely used text augmentation method [53, 54].”
Self-citationArtificial Neural Network With M5P Tree Method Based Typical Error Detection in English Language
Niu, N. (2024) · International Conference on Networks, Multimedia and Information Technology (NMITCON) · doi:10.1109/nmitcon62075.2024.10699198
“The inexperienced writer mixed the destined term with other same spelling [2].”
BackgroundDeep Learning-based English-Arabic Machine Translation for Sulfur Manufacture Texts
Hameed, D. A., & Al‐Khateeb, B. (2024) · Mesopotamian Journal of Big Data · doi:10.58496/mjbd/2024/018
Dong, Q. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934307
“Nonetheless, in NMT model the fixed length of the word vectors have better representation but the translation model failed to represent the long sentences effectively [5].”
Zhang, H. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939732
“Therefore, in inference context is past tokens through method [2].”
Dialogue-Level Data Augmentation for Conversation Derailment Forecasting and Topic-Shift Detection
Yuzbashyan, N., Banar, N., & Daelemans, W. M. P. (2024) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3711542.3711568
“Data augmentation has found application in various natural language processing (NLP) tasks: machine translation [12, 25], text generation [13] and text classification [4, 23], etc.”
RMixDA: A Random Compound Data Augmentation Operation Framework for Neural Machine Translation
Wang, H., Du, X., & Hao, X. (2025) · IEEE Access · doi:10.1109/access.2025.3580635
Huang, J. (2025) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis65944.2025.11065412
“The first is IWSLT14 German-English (De-En), comprising approximately 160,000 sentence pairs drawn from TED talks, reflecting domain mismatch and limited size[8].”
Liu, C., & Wen, Y. (2025) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl65390.2025.11085825
“The attention mechanism can be applied to the sentence coding stage and the similarity calculation stage, so as to extract and evaluate the importance of key text information [15].”
Zhang, L. (2025) · International Conference on Intelligent Computing and Robotics (ICICR) · doi:10.1109/icicr65456.2025.00083
SDGT: LLMs fine-tuning with seed-driven growth technology based on GPT-4 data expansion
Gao, D., Dai, J., Liu, S., et al. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132766
Du, Y. (2024) · International Conference on Applied Machine Learning (ICAML) · doi:10.1109/icaml64299.2024.00026
“Its structure is more streamlined yet still retains the general framework of statistical machine translation, with the key difference being the substitution of some modules with deep learning frameworks [11].”
Optimization of French Automatic Translation System Based on Neural Network
Pu, W. (2024) · International Conference on Applied Machine Learning (ICAML) · doi:10.1109/icaml64299.2024.00032
Evaluating the effect of Data Augmentation on English to Bangla Neural Machine Translation system
Datta, G. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23577-0_29
Zhu, H., Dong, Y., & Zhao, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01393-y
“This paper proposes a joint strategy of dynamic vocabulary generation and domain-adaptive fine-tuning [25, 26].”
Yu, Y., Liu, X., & Jin, L. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1894591
“If training is performed directly using random initialization, the model is prone to slow convergence, unstable semantic representation, and insufficient learning of domain features (Tonja et al., 2023; Liu et al., 2023).”
Self-citationChen, Y. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426400562
- Authors
- Liu, X., He, J., Liu, M., Yin, Z., Yin, L., & Zheng, W.
- Affiliations
- University College London (GB)
- University of Electronic Science and Technology of China (CN)
- Wenzhou University of Technology (CN)
- Guizhou University (CN)
- Louisiana State University (US)
- Corresponding author
- Liu, Mingzhe
- Zheng, Wenfeng
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2023-05-21
- Volume
- 12
- Issue
- 10
- Pages
- 2320
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4377291249
- Semantic Scholar
- 67281bf072a9299fdc2df51c3c5631f702e8eb8a
- Cited by
- 78
- References
- 37
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 15 · 2023: 45
- FWCI
- 6.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Funders
- Sichuan Science and Technology Program (2021YFQ0003, 2023YFSY0026, 2023YFH0004)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- neural machine translation, data augmentation, low-resource translation, data sparsity, reverse translation, grammatical error correction
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