Syntactic complexity as a discriminator between machine and human interpreting: A machine-learning classification approach

Authors

Keywords:

human interpreting, machine interpreting, machine-learning classification, syntactic complexity, ensemble model, SHapley Additive exPlanations

Abstract

The emergence of language technologies has positioned machine interpreting (MI) as a scalable solution to real-time multilingual communication, necessitating systematic examinations of its linguistic characteristics compared to those of human interpreting (HI) in order to foster more human-like outputs. Whereas initial studies have investigated various linguistic dimensions, syntactic complexity – a key indicator of linguistic sophistication and processing – remains under-explored in MI–HI comparisons. This study sought to bridge this gap by leveraging machine-learning classifiers to differentiate MI and HI based on multidimensional syntactic complexity metrics. We compiled a comparable Chinese-to-English corpus from government press conferences featuring HI renditions by professional interpreters and MI outputs generated by the iFlytek platform from identical source speeches. Ten machine-learning algorithms were trained on selected metrics across five dimensions, with ensemble models developed from top-performing classifiers and SHapley Additive exPlanations (SHAP) applied to quantify the importance of features. The results show that the equal-weighted ensemble of SVM, GB, and MLP yielded optimal discriminative performance (AUC = 84.29%, Accuracy = 76.15%). SHAP analysis revealed a distinct hierarchy of feature salience. Specifically, MI outputs exhibited “additive complexity”, which reflects sequential, modular processing within cascading architectures, whereas HI demonstrated “integrative complexity” derived from conceptually mediated processing that balances cognitive constraints against communicative goals. These findings advance our theoretical understanding of computational versus human language-processing mechanisms and provide empirical foundations for developing more naturalistic MI systems, thereby contributing to the emerging field exploring the interplay between interpreting and technology.

Downloads

Download data is not yet available.

Author Biographies

Yao Yao, Xi'an Jiaotong University; The Hong Kong Polytechnic University

Yao Yao is a PhD candidate in the School of Foreign Studies at Xi’an Jiaotong University and the Department of Chinese and Bilingual Studies at The Hong Kong Polytechnic University. Her research interests are computational linguistics, corpus-based translation and interpreting studies, and translation process research.

Kanglong Liu, The Hong Kong Polytechnic University

Kanglong Liu is an Assistant Professor in the Department of Chinese and Bilingual Studies at The Hong Kong Polytechnic University. His research interests include corpus-based translation studies, language and translation pedagogy, and Hongloumeng translation research. He is currently Associate Editor of Translation Quarterly, the official publication of the Hong Kong Translation Society. He has published widely in scholarly journals and authored the monograph Corpus-Assisted Translation Teaching: Issues and Challenges (Springer, 2020).

Andrew Kay-Fan Cheung, The Hong Kong Polytechnic University

Andrew K. F. Cheung is an Associate Professor at The Hong Kong Polytechnic University. He holds a PhD from the University of East Anglia and is a member of the editorial boards of Babel and Translation Quarterly. He is a member of the Association internationale des interprètes de conférence (AIIC). His research interests include quality perception of interpreting and corpus-based interpreting studies.

Dechao Li, The Hong Kong Polytechnic University

Dechao Li is a Professor of Translation and Interpreting Studies in the Department of Chinese and Bilingual Studies at The Hong Kong Polytechnic University. He also serves as the chief editor of Translation Quarterly, a journal published by the Hong Kong Translation Society. His research interests include corpus-based translation studies, empirical approaches to translation process research, the history of translation in the late Qing and early Republican periods, and PBL (problem-based learning) as well as translator/interpreter training.

References

See PDF or HTML

Downloads

Published

16-12-2025

How to Cite

Yao, Y., Liu, K., Cheung, A. K.-F., & Li, D. (2025). Syntactic complexity as a discriminator between machine and human interpreting: A machine-learning classification approach. Linguistica Antverpiensia, New Series – Themes in Translation Studies, 24. Retrieved from https://lans-tts.uantwerpen.be/index.php/LANS-TTS/article/view/822

Issue

Vol. 24 (2025): Machine and Computer-assisted Interpreting

Section

Articles

License

Copyright (c) 2025 Yao Yao, Liu Kanglong, Cheung Kay Fan Andrew, Li Dechao

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under the CC BY-NC 4.0 Deed that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. The material cannot be used for commercial purposes.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).