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In:Mathematical Modelling in Linguistics and Text Analysis: Theory and applications
Edited by Adam Pawłowski, Sheila Embleton, Jan Mačutek and Aris Xanthos
[Current Issues in Linguistic Theory 370] 2025
► pp. 149–160

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Probabilistic regularity in translation

A quantitative description of dependency treebank of translated academic abstracts

 | Xi’an Jiaotong University
Published online: 13 October 2025
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https://doi.org/10.1075/cilt.370.13lia
Abstract
Although there are many studies on translational language, they present contradictory results. This study investigates the syntactic and typological properties of translational language by adopting two main indices of dependency grammar with an aim to reveal one probabilistic translation regularity: the law of interference. After that, machine learning methods were adopted to classify translated and non-translated texts, and to further verify translation regularity on larger datasets by checking classification performance. The results show that (1) the MDD of translated English abstracts is significantly longer than that of non-translated English abstracts; (2) there are more head-final and fewer head-initial dependencies in translated than in non-translated English abstracts; and (3) compared with K-nearest Neighbor and Support Vector Machine, Random Forest achieves satisfying classification performance. These findings show the interference effect in translation and suggest that the framework of dependency grammar is helpful to reveal translation regularity.
Article outline
  • 1.Introduction
  • 2.Materials and method
  • 3.Results and discussion
    • 3.1Syntactic differences in dependency distance
    • 3.2Typological differences in dependency direction
    • 3.3The classification performance of machine learning methods
  • 4.Conclusion
  • References
References (25)
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