| Since Baker’s introduction of the research paradigm based on large-scale comparable corpora in 1993,the study of translation universals has attracted significant interest.Simplification is considered a key aspect among these universals.Despite previous efforts to develop metrics for analyzing simplification,a comprehensive measure to evaluate text complexity across various levels has not yet been established.To tackle this issue,this study integrates the concept of information entropy from natural language processing to investigate lexical and syntactic simplification in translated Chinese.I used two comparable corpora(ToRCH2014 and Yiyan)to explore the effects and interactions of translation status(translated Chinese vs.original Chinese)and four genres(press,general prose,academic,and fiction)on word entropy and part-of-speech entropy.Our aim is to identify patterns of lexical and syntactic simplification in English-Chinese translation.Our results show that information entropy is a reliable metric for assessing text complexity at both lexical and syntactic levels.Additionally,our research supports the simplification hypothesis at the lexical level,but not at the syntactic level.It’s found that translated Chinese is generally more simplified than original Chinese lexically,while its syntactic complexity is higher.This finding also supports the gravitational pull hypothesis at both lexical and syntactic levels.This study introduces information entropy as a measurement method for translation universals,providing new insights and approaches for this field.By further examining the relationship between translation universals and information entropy,the study aim to deepen our understanding of the inherent patterns and characteristics of the translation process,thereby offering useful guidance for translation practice.Furthermore,this study establishes a foundational framework for future research,fostering broader discussions and investigations for other researchers in this area. |
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