Distinguishing Translations by Human, NMT, and ChatGPT: A Linguistic and Statistical Approach

Abstract

The growing popularity of neural machine translation (NMT) and LLMs represented by ChatGPT underscores the need for a deeper understanding of their distinct characteristics and relationships. Such understanding is crucial for language professionals and researchers to make informed decisions and tactful use of these cutting-edge translation technology, but remains underexplored. This study aims to fill this gap by investigating three key questions: (1) the distinguishability of ChatGPT-generated translations from NMT and human translation (HT), (2) the linguistic characteristics of each translation type, and (3) the degree of resemblance between ChatGPT-produced translations and HT or NMT. To achieve these objectives, we employ statistical testing, machine learning algorithms, and multidimensional analysis (MDA) to analyze Spokesperson's Remarks and their translations. After extracting a wide range of linguistic features, supervised classifiers demonstrate high accuracy in distinguishing the three translation types, whereas unsupervised clustering techniques do not yield satisfactory results. Another major finding is that ChatGPT-produced translations exhibit greater similarity with NMT than HT in most MDA dimensions, which is further corroborated by distance computing and visualization. These novel insights shed light on the interrelationships among the three translation types and have implications for the future advancements of NMT and generative AI.