Applying machine translation to Chinese–English subtitling: Constraints and challenges

Abstract

Focusing on the topic of applying machine translation (MT) to Chinese–English subtitling, the subject of the present study, this article first analyses the current literature and introduces the general constraints in Chinese–English subtitling from three perspectives: technical, cultural, and textual. Technical constraints are imposed by the limited time and space available for each subtitle; cultural constraints relate to the disparities in the beliefs, values, customs, behaviours, and artefacts of different cultural groups; and textual constraints predominantly manifest in the differences between the source and target languages and the segmented nature of subtitles. In order to respond to these constraints and to achieve conciseness, comprehensibility, and coherence in the translated subtitles, this study highlighted condensation, context, and coordination as the key strategies to adopt. However, these strategies pose considerable challenges for MT in Chinese–English subtitling. First, the common practice of full translation by most MT tools tends to work against making subtitles concise. Second, the lack of relevant contextual knowledge limits the ability of MT to generate appropriate translations. Third, the segmented display of subtitles makes it challenging for MT to capture and reflect the logic in the source text, on the one hand, and to produce coherent output in self-contained segments, on the other. To illustrate these constraints and challenges, this article provides examples of bilingual subtitles from American Factory (Bognar & Reichert, 2019), an Oscar Winner for Best Documentary Feature, and compares the official subtitles with those generated by three popular MT tools in Chinese–English translation. The study investigated the efficacy of MT subtitling and its potential to produce quality subtitles; and this article proposes possible solutions and suggestions for improving the quality of MT in subtitling.