Post-editing challenges in Chinese-to-English neural machine translation of movie subtitles
Bibliographic Data
| ID | 22205212 |
|---|---|
| Authors | Chuqiao Liang (0009-0008-2455-7010, Universiti Sains Malaysia), Mozhgan Ghassemiazghandi (0000-0002-3038-3124, Universiti Sains Malaysia, corresponding author), Marlina Jamal (0000-0002-3326-1261, Universiti Sains Malaysia) |
| Year | 2024 |
| Volume | 10 |
| Pages | 100949 |
| Publication date | 2024-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Sciences & Humanities Open (JOURNAL) |
| Journal identifiers | ISSN: 2590-2911 • E-ISSN: 2590-2911 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ssaho.2024.100949 |
| OpenAlex | W4398237710 |
| Language | EN |
| Citations received | 1 |
| References cited | 16 |
Subtitle translation has been a longstanding factor hindering the overseas development of Chinese movies. The potential of using Neural Machine Translation (NMT) as an innovative solution has yet to be studied. This case study aims to integrate Google Neural Machine Translation (GNMT) into the Chinese-into-English subtitle translation of Chinese movies. The research identifies errors in GNMT-generated subtitles per Pedersen's FAR model and develops post-editing (PE) recommendations to address these errors. Firstly, the Chinese subtitles, human-translated subtitles, and GNMT-generated subtitles of a Chinese movie were collected. Then, the FAR model-based error analysis was conducted to explore the errors' features. Lastly, PE recommendations were proposed accordingly to modify these errors. Approximately a quarter of all subtitles contain errors, with functional equivalence errors the most prevalent (about half), followed by acceptability errors (about a third) and readability errors (14%). Regarding the severity of errors, standard errors rank first (42%), followed by serious errors (30%) and minor errors (28%). Subtitlers should focus on semantic, idiomaticity, grammar, and line length errors caused by incorrect translation of proper nouns, cultural-bound terms, incomplete sentences, etc. By exploring the features of errors and PE recommendations for GNMT of Chinese movie subtitles, this study offers a valuable framework for improving the quality of NMT-generated subtitles and presents a new solution to overcome the language barriers faced by the Chinese film industry
Machine translation · Natural language processing · Speech recognition · Chemistry · Computer Science · Natural Language Processing Techniques · Subtitles and Audiovisual Media · Translation Studies and Practices · Artificial Intelligence
English, Chinglish or China English
Quality of Post-Edited Interlingual Subtitling
A reception study of machine translated subtitles for Moocs
Assessing the quality of post-edited wildlife documentaries
Post-editing machine translated subtitles
China’s digital media industries and the challenge of overseas markets
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |