Translation and context adaptability study of renewable energy technology terms in English and Chinese
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Junhong Ren
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Leijiang Su
Abstract
Translation is the process of converting a text sequence from one language to other. Translation systems are often used to translate between various language texts, but they may also be used for speech or a mix of the two, such as text-to-speech or speech-to-text. Statistical machine translation (SMT) employs large target language models (LMs) to increase the fluency of produced texts, and it is often considered that “more data is better data” when developing language models. This work focuses on translating the Renewable Energy (RE) terms in English and Chinese languages. The developed work mainly focuses on two models namely; Translation language model and Context adaptability model. Initially, a model is developed that is capable of translating renewable energy terms from English to Chinese. Subsequently, context adaptability model is built, where, two processes are carried out. The two processes include, (a) building a word corpus related to RE and (b) building dictionaries related to RE. The dictionaries are built by generating sentiment lexicons using Pointwise Mutual Information (PMI) model. Finally, the language is trained using improved BERT model.
Funding source: Study of Academic English Teaching Strategies From the Perspective of Grammatical Metaphor
Award Identifier / Grant number: 1108/9160823002
Acknowledgments
The authors would like to show sincere thanks to those techniques who have contributed to this research.
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Research ethics: This article does not contain any studies with human participants performed by any of the authors.
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Informed consent: Not applicable.
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Author contributions: Junhong Ren, Aihui Wang, is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. Leijiang Su, is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declared that they have no conflicts of interest regarding this work.
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Research funding: Central University Targeted Fund: Study of Academic English Teaching Strategies From the Perspective of Grammatical Metaphor, Serial No.:1108/9160823002.
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Data availability: The experimental data used to support the findings of this study are available from the corresponding author upon request.
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