Methodology Matters: How Translation Theories and Technologies Have Shaped Journey to the West

Abstract

Translation between Chinese and English demonstrates that different language pairs are more difficult for humans and machines to  translate. Additionally, difficulties in the translation world center around literary translation, the topic of the Workshop on Machine Translation’s (WMT) shared discourse taskforce. The main conference on Machine Translation and MT research, WMT 2023 focused on literary translation, specifically the challenges posed by Chinese literature. The use of entirely vernacular language combined with other literary, cultural, and historical traditions in Xīy.uj. (西游 or Journey to the West), poses a unique translation dilemma. Translation is best described as a spectrum of theories that influence a translator’s approach to their work ranging from dynamic to literal. Machine Translation (MT), however, is governed by different approaches based on engineering programs that mimic natural language production. Comparing human and machine translation approaches to Chinese literature can improve classical Chinese-to-English translations through a comparison of translation techniques. This research will focus on the most influential translators of Classical Chinese, Arthur Waley, Anthony C. Yu, and William J. F. Jenner. The influences of a translator’s approach to their work through the lens of Liraz Postan’s categories and the methodologies of various machine translation engine types are examined with particular interest.

Keywords: Rules-based machine translation (RBMT), Statistical machine translation (SMT), Neural machine translation