A Comparative Study of AI translation - Google Translate and Papago

다국어 초록 (Multilingual Abstract)

Since the mid 2010s, there have been a number of studies on the reliability of the translation capability of AI translation programs. Shin (2017) notes how machine translation systems have developed from a translation researcher’s perspective. Johnson et al. (2016) proposes a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Kim (2017) discusses how human spiritual aspect can be kept intact in the AI technology-dominated society. Kwak et al. (2020) conducted a study on AI translation focusing on proverb translation. Besides, Bahdanau et al. (2015) and Chung (2020) conducted research on machine translation. Following Park (2017, 2018)’s seminal research on the accuracy of the translation capability of Google Translate, this study investigates whether Google Translate and Papago, a translation program developed in Korea, have acquired native-like ability in translating sentences with semantically ambiguous Korean expressions such as –ya hayssta (had to vs was supposed to), -su issessta (could vs was able to), construction involving negation and sayngkakhata (think), mitta (believe), nukkita (feel) + embedded clause, and –cul alassta (knew or thought).

목차 (Table of Contents)

• 1. Introduction
• 2. Tests
• 3. Conclusion
• References