Translation and context adaptability study of renewable energy technology terms in English and Chinese

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.