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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 24, No. 0, pp.257-276
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Jan 2024
Received 20 Jan 2024 Revised 27 Feb 2024 Accepted 25 Mar 2024
DOI: https://doi.org/10.15738/kjell.24..202404.257

A Corpus-based Multilingual Comparison of AI-based Machine Translations

Cuilin Liu ; Se-Eun Jhang ; Homin Park ; Hyunjong Hahm
(First author) PhD student National Korea Maritime & Ocean University Claire1182904043@outlook.com
(Corresponding author) Professor National Korea Maritime & Ocean University jhang@kmou.ac.kr
(Co-author) Researcher Electronics and Telecommunications Research Institute hominpark@etri.re.kr
(Co-author) Associate Professor University of Guam hhahm@triton.uog.edu


© 2024 KASELL All rights reserved
This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The present study aims to investigate whether, and to what extent, the corpus linguistic technique type-token ratio (TTR) is valid in identifying the quality of translation productions produced by different AI-based machine translation (MT) systems. Specifically, this study examined the discourse-level discrepancies of MT outputs generated by Google Translate, DeepL and ChatGPT 3.5 on the discourse level utilizing a self-complied multilingual corpus of English translations for the short story Eveline in Korean and Chinese. For this purpose, we calculated the TTR separately for different text segments within a moving span of running word-tokens and visualized the results with a two-dimensional approach. In addition, to verify the validity of this TTR method in predicting the discrepant qualities of the three MT systems, we took a comprehensive reference of three metrics (Bilingual Evaluation Understudy, BLEU; Metric for Evaluation of Translation with Explicit Ordering, METEOR; Recall-Oriented Understudy for Gisting Evaluation, ROUGE) that are commonly used to evaluate the quality of MTs. The paper demonstrated the validity of TTR graphs in assessing the quality of a particular MT system. The findings corroborate the argument in previous studies that AI-based MT produced less lexical diversity and information density.

Keywords:

type-token ratio (TTR), span, text structure, machine translation, Google Translate, DeepL, ChatGPT 3.5

Acknowledgments

The first draft of this paper was presented orally at the KACL-KASELL Summer Joint Conference, Korea University on June 3, 2023. Some parts of the first draft were also presented orally at the Joint Forum between Korea Maritime & Ocean University and The University of Kitakyushu held at The University of Kitakyushu, Japan on July 27, 2023.

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