A Comparative Study on Transformer Versus Sequence to Sequence in Machine Translation

Abstract

Inspired by the increasing interest in leveraging large language models for translation, this paper evaluates the capabilities of large language models (LLMs) represented by ChatGPT in comparison to the mainstream neural machine translation (NMT) engines in translating Chinese diplomatic texts into English. Specifically, we examine the translation quality of ChatGPT and NMT engines as measured by four automated metrics and human evaluation based on an error-typology and six analytic rubrics. Our findings show that automated metrics yield similar results for ChatGPT under different prompts and NMT systems, while human annotators tend to assign noticeably higher scores to ChatGPT when it is provided an example or contextual information about the translation task. Pairwise correlation between automated metrics and dimensions of human evaluation produces weak and non-significant results, suggesting the divergence between the two methods of translation quality assessment. These findings provide valuable insights into the potential of ChatGPT as a capable machine translator, and the influence of prompt engineering on its performance.