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A Study of the Usability of the AI Learning Corpus Data Provided by AI Hub: Focusing on ChatGPT’s TQA

  • The Journal of Translation Studies
  • Abbr : JTS
  • 2023, 24(4), pp.129-169
  • DOI : 10.15749/jts.2023.24.4.005
  • Publisher : The Korean Association for Translation Studies
  • Research Area : Humanities > Interpretation and Translation Studies
  • Received : November 15, 2023
  • Accepted : December 18, 2023
  • Published : December 31, 2023

Kwak, Eun Joo 1,  Jaehoon Noh 2,  Mijin Park 3,  Chun, Hyun-ju 4

1세종대학교
2㈜와이즈에스티글로벌
3(사)국제통역번역협회
4신한대학교

Accredited

ABSTRACT

This study attempted to verify the usability and applicability of the ‘AI Learning Corpus’ released by AI Hub as test data for quality assessment of the translation service provided by ChatGPT. For this purpose, the translation quality of ChatGPT and MT was evaluated using N-gram-based and segment-wise BLEU score analysis methods. As a result of the Translation Quality Assessment (TQA), the general-purpose MT, which is the subject of comparison, showed higher consistency and correlation with MTPE, a reference translation, in terms of the BLEU score index compared to the generative AI ChatGPT. In light of the fact that ChatGPT’s main purpose of use includes translation, the BLEU score measurement results showed, on the contrary, that the quality of general-purpose machine translation MT was high. The fundamental reason for the gap between MT_Basic and ChatGPT’s BLEU scores consistently presented in the analysis results is that hallucination issues may occur in ChatGPT’s translation quality judgment, especially depending on the platform user’s experience. It is judged to be a special achievement of the research.

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