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Mirror your words, but NOT your wordings

A network approach to human translation and machine translation

 | Zhejiang University
 | Tongji University
ORCiD logo with link | Zhejiang University
This content is being prepared for publication; it may be subject to changes.
Abstract
Current research has demonstrated the potential of large language models, such as GPT, as powerful translation tools. However, gaps remain in understanding how human and machine translation differ across linguistic and structural levels. This study adopts a network-based approach, using syntactic dependency networks to investigate structural differences in translations produced by humans and machines (Google Translate and ChatGPT). The findings revealed that human translation networks exhibit higher clustering coefficients and shorter average path lengths compared to ChatGPT translations, along with lower density and degree centrality than Google Translate. Human translations also contain fewer function words among central nodes than machine translations. These findings suggest that while machine translation prioritizes producing grammatically well-formed sentences, human translation tends to be more concise and efficient in transmitting information, optimizing the balance between syntactic simplicity and communicative clarity. Machine translation mirrors human words but does not fully replicate the syntactic and structural patterns of human translation. Our study offers new insights for future studies on translation in the context of AI.
Article outline
  • 1.Introduction
  • 2.Research design
    • 2.1Construction of the corpus
    • 2.2Construction of syntactic dependency networks
    • 2.3Indicators of complex networks
  • 3.Data analysis and results
    • 3.1Scale-free and small-world properties of HT, GT, and GPT networks
    • 3.2Disparities in main indicators of HT, GT, and GPT networks
    • 3.3The proportion of function words as hubs in HT, GT, and GPT networks
  • 4.Discussion
    • 4.1Scale-free and small-world properties of HT, GT, and GPT networks
    • 4.2Similarities and disparities in main indicators of HT, GT, and GPT networks
    • 4.3The proportion of function words as hubs in HT, GT, and GPT networks
  • 5.Conclusion
  • Author Contributions
  • Notes
  • References
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